Summarization of specialized discourse: The case of medical articles in Spanish

Summarization of specialized discourse: The case of medical articles in Spanish
Summarization of specialized discourse
The case of medical articles in Spanish
Iria da Cunha, Leo Wanner and Teresa Cabré
In this article, we present the current state of our work on a linguistically-motivated model for automatic summarization of medical articles in Spanish. The
model takes into account the results of an empirical study which reveals that,
on the one hand, domain-specific summarization criteria can often be derived
from the summaries of domain specialists, and, on the other hand, adequate
summarization strategies must be multidimensional, i.e., cover various types of
linguistic clues. We take into account the textual, lexical, discursive, syntactic
and communicative dimensions. This is novel in the field of summarization. The
experiments carried out so far indicate that our model is suitable to provide high
quality summarizations.
Keywords: summarization, extraction, medical article, linguistic analysis,
Spanish, specialized discourse
.
Introduction
Already in 1970, some authors identified the “information overload” faced by
modern society (Toffler 1970). This overload dramatically increased since the
Internet became our daily companion. It is de facto impossible to browse even
a reasonably small share of the material with the goal to filter out information
that is relevant to us, let alone to read it. Professionals in research intensive domains are especially affected. They experience a real predicament when they need
to know the state of the art in their area in order to account for their tasks. On
the other hand, they often simply do not have the time for extensive reading. Automatic summarization is likely to be of great use for these professionals since it
facilitates the “distillation” of the essence of the information material, allowing
thus the reader to assess the relevance of the material and to grasp its meaning in
the shortest possible time. However, it is important to note that techniques used
Terminology 13:2 (2007), 249–286.
issn 0929–9971 / e-issn 1569–9994 © John Benjamins Publishing Company
250 Iria da Cunha, Leo Wanner and Teresa Cabré
for automatic summarization of general discourse are not necessarily suitable for
summarization of specialized discourse. Recent works in the field show that high
quality automatic summarization of specialized discourse must take into account
the characteristics of the discourse in question (Teufel and Moens 2002; Johnson
et al. 2002; Farzindar et al. 2004). These characteristics concern both the way the
content is presented and the way material is to be summarized. Journals in certain
specialized discourses (as, e.g., in the medical discourse), tend to have detailed
guidelines for how to write a summary and what it should contain.
In this article, we address the problem of automatic summarization of medical
articles in Spanish from the extraction angle.1 For medical professionals, articles
are an important source of information. Several types of articles are being published: Original Articles, Short Original Articles, Revisions, Clinical Notes, etc. We
focus on Original Articles and Short Original Articles (henceforth, referred to as
“medical articles”).
Given that existing proposals for automatic summarization of medical articles
tend to be based on general purpose summarization strategies (cf. Afantenos et al.
2005 for a state of the art study), our goal has been twofold: firstly, to verify whether
the summaries of medical articles reveal different characteristics than summaries
of general purpose discourse, and if yes, to identify these characteristics; secondly,
to develop a summarization model that takes into account the criteria that have
been identified before.
To assess the degree of idiosyncrasy of medical article summaries and to evaluate whether the summaries by domain specialists (i.e., medical doctors) are, on
the one hand, similar among each other and, on the other hand, significantly divergent from the summaries by specialists in general discourse analysis and writing (i.e., linguists), we undertook an empirical study. The results of the study reveal
that, indeed, adequate summaries of medical articles show major idiosyncrasies
which must be taken into account during automatic summarization.
In the next stage of our work, we identified the criteria that need to be taken into account in summarization oriented towards medical articles (cf. also da
Cunha and Wanner 2005 for a short preliminary presentation). In general, we
found that the summarization model must
–
–
Take into account all domain-specific summarization criteria (those that are
available explicitly in terms of author guidelines and those that can be derived
studying human written summaries);
Consider the whole range of linguistic dimensions reflected in an article: textual, discursive, communicative,2 lexical, and syntactic.
In the course of the final stage, we developed a multidimensional summarization
model that integrates all of the above criteria — in contrast to most of both gen-
Summarization of specialized discourse 25
eral purpose and domain-specific models which focus on one or two linguistic
dimensions only. The evaluation of the experiments carried out so far shows that
automatic summaries produced by the application of our model are comparable
with the author written summaries. We are confident that the design principles
underlying our model can be ported to other domains.
The remainder of the article is structured as follows. In the next section (Section 2), we review the state of affairs in automatic summarization in general discourse, in specialized discourse in general, and in the medical domain in particular. Section 3 analyzes the predefined text structure of a medical article. Section 4
describes first the empirical study that we carried out to verify the distinctiveness
of medical article summaries and to identify the characteristics that have to be
taken into account, and draws then the conclusions for our model. In Section 5,
we present a description of our model. In Section 6, the experiment that validates
the model is described. Section 7, finally, recapitulates the essentials of the article
and suggests some lines of future work.
2. Automatic summarization
Although the summarization of specialized discourse received a certain attention
already since the very beginnings of the automatic summarization research (e.g.,
Luhn 1958), the focus in the field over the years has been on general discourse.
Thus, it is not surprising that strategies developed for general discourse have often
also been applied to specialized discourse and, in particular, also to summarization
of medical articles. In this section, we consider only extraction-oriented works.3
2. Summarization of general discourse
Extraction-oriented strategies of summarization may be classified in terms of surface-oriented strategies, medium-level strategies and deep strategies (Radev et al.
2002). Many of them are “mono-dimensional” in that they draw upon one specific
type of criteria to identify summary relevant text passages.
Surface-oriented strategies tend not to use any (or use only rudimentary)
linguistic information of the textual material to be summarized. Well-known are
techniques that use positional or cue phrase criteria. The positional criteria specify
where — at the beginning of the first or last section, following specific section
headings, etc. — text chunks that are relevant to the summary are located (see, e.g.,
Brandow et al. 1995; Lin and Hovy 1997). Cue phrases such as “It is important to
note that …”, “To conclude …,” etc. are supposed to introduce summary relevant
text chunks (Edmundson 1969).
252 Iria da Cunha, Leo Wanner and Teresa Cabré
Of similar level of abstraction are techniques that use statistical criteria of
varying complexity: simple frequency of terms (Edmunson 1969), Bayesian models (Kupiek et al. 1995), Maximal Marginal Relevance (Goldstein et al. 1999), etc.
Medium-level strategies make use of certain linguistic, mostly lexical or surface-syntactic, information. Lexical chains (sequences of words between which
lexico-semantic relations hold) received particular attention (Barzilay and Elhadad 1997; Silber and McCoy 2000).
Deep strategies draw upon more profound linguistic information such as discourse structure. Discourse structure (modelled, first of all, in terms of the Rhetorical
Structure Theory (RST), Mann and Thompson 1988) proved to be of special importance since it ensures the coherence of the summary if the latter is obtained by cutting
off selected branches of the discourse tree (Ono et al. 1994; Marcu 2000).
2.2 Summarization of specialized discourse
It is well-known that summarization must be considered in the light of the text
genre to be summarized. For instance, news summaries must contain the global
event settings and the most relevant details; novel summaries must contain the
general outline of the plots, without revealing details and the outcome, etc. As
Kaplan et al. (1994), who research abstracts in the area of applied linguistics, point
out, summaries must be differentiated with respect to their purpose, targeted audience and extension. The same argumentation is adopted by several other authors.
Thus, Teufel and Moens (2002) argue that the summary of a scientific article must
primarily capture the novelty of the work (given that the purpose of a scientific
article is the presentation of new scientific research), while a general purpose summary is usually supposed to present a short overview of the content of the material in question. Ciapuscio (1998) argues along similar lines that the summary of
a scientific submission is written with the goal to convince the reviewers that the
submission is novel and should thus be accepted for presentation or publication.
Despite this cognizance, the main focus of the research on automatic summarization in the past has been on general discourse — with a few exceptions
such as the early experiments by Luhn (1958), who used term frequency to determine summary relevant sentences in technical material, and Pollock and Zamora
(1975)’s work, which presents an extraction algorithm for automatic summarization of texts on chemistry.
Starting from the nineties, an increasing number of researchers focused on the
automatic summarization of specialized discourse (cf., among others, Paice 1990;
Riloff 1993; Lehmam 1995; McKeown and Radev 1995; Abracos and Lopes 1997;
Saggion and Lapalme 2000) — although often using the same strategies as for the
summarization of general discourse. Especially summarization of news had been
Summarization of specialized discourse 253
an issue. For instance, Abracos and Lopes (1997) use statistics to select the most
important content of newspaper articles. McKeown and Radev (1995) do multidocument abstracting of news statements on specific themes (such as terrorist
attacks) using knowledge templates known from Information Extraction.
Some recent works on specialized discourse in fact do address the specifics of
the genre in question. Thus, as already mentioned above, Teufel and Moens (2002)
suggest a summarization strategy of scientific articles (more precisely, contributions to conferences on computational linguistics) that is based on the rhetorical
status of each declaration included in the article in question. The material to be
included in the summary is selected taking into account that the summary of a
scientific article must highlight the novelty of the described work and contrast it
to the state of the art. Teufel and Moens present an algorithm based on annotated
training material that first classifies the material to be summarized in terms of
seven rhetorical categories and chooses then the content to be included in the
summary in accordance with these categories.
Farzindar et al. (2004) address the problem of automatic summarization of
specialized juridical documentation. Their goal is to help experts of this area to determine the key ideas of a judgment in order to find other documents that could be
relevant. Farzindar et al. use the text structure to automatically construct a coherent summary.4 More precisely, they construct a summary in four phases: (1) text
structure segmentation that identifies the five topics of the document structure:
Decision Data, Introduction, Context, Juridical Reasoning and Conclusion; (2)
elimination of summary irrelevant information (as, e.g., citations of legal articles);
(3) selection of relevant chunks from the four last sections using specific linguistic
markers and textual position criteria for each of these topics; and, finally, (4) aggregation of the selected chunks applying a length limit.
2.3 Automatic summarization of medical documentation
As pointed out by Afantenos et al. (2005), automatic summarization of medical
material became recently a very prominent research topic. Both extraction and
abstraction paradigms are being followed. Consider Kan et al. (2001), Damianos
et al. (2002), Lenci et al. (2002), Johnson et al. (2002) for proposals within the
extracting paradigm and Gaizauskas et al. (2001), and Kan (2003) for proposals
within the abstracting paradigm.
As already in the case of proposals presented in the previous section, most of
the proposed techniques are general purpose summarization techniques adapted
to medical articles. Typical criteria drawn upon by extraction techniques are surface criteria:5 text structure, cue phrases, sentence positioning and named entities.
Some techniques combine surface-guided extraction with clustering algorithms
254 Iria da Cunha, Leo Wanner and Teresa Cabré
used to identify key features for semantically related articles. The summary is then
built with sentences containing these key features (e.g., Johnson et al. 2002). None
of these techniques uses, to our knowledge, more elaborate linguistic criteria such
as syntactic dependency relations, communicative structure or discourse structure.
3. Medical articles in Spanish: Their structure and content distribution
Before we set out to study how medical articles in Spanish are most adequately
summarized, let us analyze the default structure of a Spanish medical article and
the distribution of the content across the article. In our analysis, we follow the
guidelines of Medicina Clínica, which is the most widely read Spanish medical
journal.
As already mentioned in the Introduction, we focus on two types of articles:
Original Article and Short Original Article. Medicina Clínica requests authors of
both types of articles to adopt the following text structure: Title, Spanish Abstract,
Spanish Keywords, English Abstract, English Keywords, Introduction, Patients and
methods, Results, Discussion, Acknowledgments (optional) and Bibliography. The
core structure is thus the so-called “IMRD structure” (Swales 1990),6 which is supIntroduction: must be brief, providing only the information necessary for the comprehension of the following sections. It must not contain tables or figures, but it must include clearly
stated goal(s) of the work. If the authors claim to publish a previously unnoticed observation,
they must outline in the Introduction the method of the bibliographic search for the state of
affairs, the used keywords, the years of coverage and the date of the last search.
Patients and methods: must contain the location where the experiment or the research
has been carried out, the duration of the experiment, characteristics of studied series, used
criterion for selection and the techniques used. Sufficient details must be provided such that
a specific experiment can be repeated on the basis of this information. Statistical measures
applied in the experiment must be described in detail.
Results: must contain the presentation of the findings obtained by the method without that
any interpretation of these findings is added; if adequate, the findings must be complemented
by tables and figures.
Discussion: must contain the opinion of the authors on the findings, and in particular: 1)
meaning and practical application of the results; 2) considerations of a possible inconsistency
of the methodology and reasons for which results are valid; 3) relation with similar publications and comparison between areas of agreement and disagreement, and 4) indications and
guidelines for future research. The discussion must not contain any revision of the state of the
art (as in the Introduction). Results of the work must not be repeated either.
Summary: must contain the essence of all four IMRD sections.
Figure 1. Author guidelines for articles in Medicina Clínica
Summarization of specialized discourse 255
posed to reflect the logical order common to scientific argumentation. For the
composition of each section within the core structure, guidelines are given; cf.
Figure 1 above.
4. How should a summary of a medical article in Spanish look like?
The guidelines in Figure 1 give a general idea of how a summary of a medical
article should look like. Based on this idea, an abstract-oriented strategy similar
to the one suggested by McKeown and Radev (1995) could be drafted. However,
an abstract-oriented strategy would require deep linguistic analysis techniques,
which are costly to develop and rather unreliable. Therefore, we opted for an extraction-oriented strategy.
For the design of a well-motivated extraction-oriented strategy for summarization of specialized discourse (here, Spanish medical articles), two related questions (of different levels of abstraction) seem central:
1. Do the summaries of Spanish medical articles follow the same lines as the
summaries of general discourse?
2. If yes, can any of the existing strategies be applied to our task? If not, what are
the relevant genre-specific summarization criteria?
Although we expected that the first question is to be answered in the negative, we
needed this expectation to be confirmed by empirical figures. For this purpose,
we carried out an experiment that indeed fully confirmed our expectation. In the
course of this section, we first describe this experiment and then assess the outcomes of the experiment with respect to the construction of our summarization
model (and, thus, with respect to the second question from above).
4. Experiment in summarizing medical articles
If the summarization of medical articles requires the consideration of the genre,
this should be reflected within the summaries written by the authors or other
professionals of the field — in contrast to the summaries of non-professionals or
specialists of general discourse as, e.g., linguists. In other words, the summaries
of professionals should be similar among each other and different from the summaries of non-professionals.
This is the basic idea underlying our experiment (for an initial outline of the
experiment, see da Cunha and Llopis, in print).
256 Iria da Cunha, Leo Wanner and Teresa Cabré
4.. General Setting of the Experiment
The experiment involved the participation of six medical doctors and six linguists.
All 12 subjects were asked to provide summaries of 20 Spanish articles.7 For each
article, we had an author written summary as reference.
The 12 subjects were given the 20 medical articles (without the abstracts of the
authors) and asked to mark in each article the content they considered essential
for inclusion into the summary. To control the length of the resulting summaries,
the subjects were asked to observe a limit of the lines of the text they underline (20
lines were suggested). Figure 2 presents the summarization instructions.
Underline in each text the content chunks (sentences, phrases or fragments of text) that you
consider essential for inclusion in the summary (do not consider titles and subtitles).
For a summary of an Original Article, do not underline more than (approx.) 20 lines in
total.
For a summary of a Short Original Article, do not underline more than (approx.) 15 lines in
total.
Figure 2. Instructions for carrying out the summaries in the experiment
The content chunks marked by any of the subjects in accordance with the instructions given in Figure 2 have been represented in a content selection table, assigning
to each chunk a variable; in total 624 chunks have been identified. This table served
as basis for the assessment of the similarity, respectively difference, between the summaries obtained from the subjects as well as the authors; cf. Table 1 for a fragment
that illustrates the composition of the table. The first column contains the different
content chunks. In the second column, each chunk is assigned a variable used for
further processing. The remaining columns contain information on whether the corresponding subject has chosen the chunk in question for inclusion in the summary
or not (‘1’ stands for inclusion, ‘0’ for omission). ‘A’ stands for the author, ‘M’ for one
of the medical doctors and ‘L’ for one of the linguists. The indices associated to ‘M’
and ‘L’ identify the corresponding medical doctors and linguists, respectively.8
4..2 Quantitative Assessment of the Experiment Outcome
To quantify the degree of similarity, respectively difference, between the summaries, we used the techniques of Multidimensional Scaling and Unsupervised Clustering as implemented in Statgraphics, which is a widely available off-the-shelf statistical program.
Multidimensional Scaling (MS) is a dimension reduction technique which
maps a d-dimensional space onto a 2-dimensional space, attempting to find the
most similar representation of the original d-dimensional cloud of points in a
plane. In our case, d is the number of variables we work with (i.e., d = 624). The
procedure consists, in fact, of the representation of the studied items in the two
Summarization of specialized discourse 257
Table 1. A fragment of the table with content chunks chosen by at least one subject
content
V
A M1 M2 M3 M4 M5 M6 L1 L2 L3 L4 L5 L6
Entre el 20 y el 80% de las visitas
a los servicios de urgencias hospitalarios son inapropiadas.
X1-1
1
1
1
1
1
1
1
1
1
1
1
1
0
Se evaluaron las visitas a un servicio de urgencias hospitalarias
mediante un Protocolo de Adecuación de Urgencias Hospitalarias, previamente validado.
X2-1
1
1
1
1
1
1
1
1
1
1
1
1
0
El 37,9% de las visitas fueron
inapropiadas
X3-1
1
1
1
1
1
1
1
0
0
1
1
0
1
y más frecuentes en la población
pediátrica.
X4-1
1
1
1
1
1
1
1
1
1
1
1
1
1
Los pacientes enviados por un
médico, con traumatismos o
proceso quirúrgico visitaron las
urgencias más adecuadamente.
X5-1
1
1
1
1
1
1
1
1
1
1
0
1
1
Estudio descriptivo sobre una
muestra aleatoria representativa
de los 84.329 pacientes atendidos
en urgencias durante el año 1999.
X7-1
0
1
1
1
1
0
0
0
0
0
0
0
0
…
…
… … … … … … … … … … … … …
first main dimensions of a Principal Component Analysis. These two main dimensions grasp a maximum of variability (of information). Thanks to that, reduction
of dimensions is carried out minimizing the loss of information.
The outcome of the MS-procedure is presented in Figures 3 and 4.9 The first
main dimension of the MS-model is plotted as the vertical (y) dimension, the
second as the horizontal (x) dimension. Figure 3 shows the overall similarity between all summaries (i.e., subject summaries and authors’ summaries), or, in other
words, the similarity calculated over all 624 variables of the content selection table.
According to the diagram in Figure 3, the 6 doctors and the authors of the articles
select very similar content for inclusion into the summary, but rather different
from the content selected by the linguists (the summaries by the medical doctors
are circled). On the other hand, although several linguists coincide largely with
respect to their selection criteria, the dispersion among the summaries of the linguists is larger.
Figure 4 shows how similar the subject summaries are to the summaries of the
authors. For this latter presentation, MS thus examined to what extent the selection preferences of our subjects coincided with the choices of the authors, without
258 Iria da Cunha, Leo Wanner and Teresa Cabré
Figure 3. Overall similarity between the
subject and author summaries
Figure 4. Contrast of the content of the
authors’ summaries to the content marked
for inclusion into the summary by the 12
subjects
taking the coincidence between the selection preferences among the subjects into
account. This second examination was carried out to verify, on the one hand, to
what extent the subject summaries included content chunks also found in authors’
summaries, and, on the other hand, to assess to what extent the subject summaries
contained further chunks not available in the summaries of the authors (i.e., to
what degree the length of the subject summaries exceeded the length of the author
summaries).10 It demonstrates that not only the content chunks selected by the
medical doctors tend to coincide, but that these chunks also tend to coincide with
the chunks selected by the authors. The summaries of the linguists, on the other
hand, again, show a rather different, dispersed pattern.
To confirm the outcomes of Multidimensional Scaling, we further assessed the
similarity of the subject and author summaries by Hierarchical Unsupervised Clustering. Unsupervised Clustering (UC) is a technique that groups data in terms of a
group hierarchy (instead of a flat classification) according to the distance between
them — without using any type of external information to organize the groups: at
each level of the hierarchy, pairs of groups that are sufficiently similar to each other
form a generalized group (which is then again used as a group).
The resulting classification is shown in terms of dendograms in Figures 5 and
6. The dendograms display group hierarchies built from the 13 group nodes (1
author group + 6 medical professional groups + 6 linguist groups) in our experiment. The length of the branches of the dendograms (the vertical axes) displays
the distance between the different group nodes. The left part in Figures 5 and 6
captures the author group and the 6 medical professional groups; the right part
— the 6 linguist groups. I.e., UC confirms the similarity between the summaries
of the authors and the summaries of the medical doctors on the one hand, and
between the summaries of the linguists on the other hand.
As can be observed, the results are also similar to the results obtained by Multidimensional Scaling.
Summarization of specialized discourse 259
8
6
10
8
6
4
4
2
2
0
0
Figure 5. Overall similarity between the
subject and author summaries
Figure 6. Contrast of the content of the
authors’ summaries to the content marked
for inclusion into the summary by the 12
subjects informants
In the next section, we discuss the very pronounced similarity pattern between the summaries of the medical doctors and the deviances of these summaries
from the summaries of linguistic professionals in more detail.
4..3 Qualitative Assessment of the Experiment Outcome
The Multidimensional Scaling and Clustering experiments described above confirm the high degree of coincidence among the summaries by medical professionals (informants and authors) and their deviance from the summaries by linguists.
Let us now analyze the content chosen by the two groups.
Firstly, we observe a general trend among linguists to include too much information from the Introduction section into their summaries. We hypothesize that
this is due to their lack of formation in medicine, which makes them consider term
definitions, historical data, confirmation of facts, references to previous works,
etc. relevant to the summary. In contrast, medical doctors did not choose this type
of content for their summaries because it appears to them either trivial or wellknown in the field.
Secondly, we observe that medical professionals select balanced information
from each of the four sections (Introduction, Patients and methods, Results and
Discussion) with the goal to reflect in the summary the IMRD-structure of the article. The linguists, although aware of the fact that a medical summary must follow
the IMRD-structure, did not follow this guideline strictly.
Thirdly, we observe that medical doctors usually select numerical information, especially in the section of Patients and methods and Results, whereas linguists tend to avoid the inclusion of numerical information. Instead, they favour
more explanations and fewer figures.
Finally, we clearly see that linguists preferably select content from the Discussion section, whereas medical doctors normally include into the summary a much
briefer account of the Discussion.
260 Iria da Cunha, Leo Wanner and Teresa Cabré
Table 2. Cases and significant examples of different content chunks selected by medical
doctors and linguists
Case
N
Introduction 1
2
3
4
Information 5
from each of
the 4 sections
Examples
Drs
Lings
- Definitions:
0%
Ex. “C. difficile es un bacilo grampositivo anaerobio, productor de esporas,
que puede colonizar a pacientes hospitalizados, en especial a ancianos, tras
0%
tratamiento antibiótico.”
Ex. “El paraquat es un herbicida bipiridilo.”
100%
- Historical data:
Ex. “Desde que a finales de la década de los años cincuenta se empezaron
a utilizar los primeros fármacos antihipertensivos se ha producido una
eclosión imparable de nuevos fármacos hasta nuestros días.”
Ex. “La osteoporosis y su complicación clínica, las fracturas, han despertado en los últimos años un gran interés no sólo por su alta morbimortalidad sino también por su estrecha relación con el envejecimiento
poblacional.”
0%
100%
0%
100%
- Confirmation of facts:
Ex. “La infección por el VIH en España ha estado focalizada principalmente en los usuarios de drogas inyectadas, y a partir de esta población se
ha ido extendiendo de forma secundaria por vía sexual y perinatal.”
Ex. “En la actualidad, el cáncer de mama constituye un problema de gran
importancia sanitaria en los países desarrollados.”
0%
100%
0%
100%
- Previous works:
0%
Ex. “La caracterización molecular de la cistinuria comenzó a principios de
los noventa cuando Calonge et al. demostraron que varias mutaciones en
el gen SLC3A1 estaban asociadas a la cistinuria humana.”
Ex. “Sempere et al. han validado un Protocolo de Adecuación de Urgencias 0%
Hospitalarias (PAUH) para detectar visitas inadecuadas a este servicio.”
100%
100%
100%
Ex. “Con la RM-mielografía se obtuvo información nueva en 81 casos
(32%).”
100% 0%
Ex. “La progresión a sida en España hasta 1996 y el efecto de la edad son
similares a otras cohortes europeas.”
100% 0%
…
Patients and 6
methods and
Results
(Numerical
information)
Ex. “La prevalencia de anticuerpos anti-VIH fue de 0,99 por 1.000 en 1996, 100% 0%
1,29 en 1997, 1,42 en 1998 y 1,54 en 1999.”
Discussion
Ex. “Dado el aumento de la prevalencia del VIH en madres de recién naci- 100% 100%
dos, son necesarios el consejo y la oferta sistemática de la prueba del VIH
a todas las mujeres embarazadas.”
7
0%
Ex. “Las lesiones bucales, faríngeas y/o esofágicas fueron visibles en
algunos pacientes ya en el momento del ingreso, pero en otros aparecieron
días después.”
Ex. “La seroprevalencia del VIH en madres de recién nacidos obtenida en
este estudio no puede considerarse representativa de toda España.”
Ex. “Aun así, las seroprevalencias encontradas son superiores a las descritas en otros países europeos.”
Ex. “El patrón geográfico es muy similar al de la incidencia de casos de
sida.”
…
… …
100%
0%
16’6%
0%
16.6%
0%
16.6%
…
…
Summarization of specialized discourse 26
Table 2 summarizes the material on which our analysis is based in terms of
some representative examples from the corpus. It shows a number of content
chunks that have been selected, respectively omitted by medical professionals and
linguists. The first column (Case) refers to the case that is illustrated, the second
to the number that is assigned to each case (N), the third (Example) offers specific
examples of the case in question; the fourth (Drs) and the fifth (Lings) indicate the
percentage of medical doctors and linguists who selected these examples.
The quantitative and qualitative evaluation of summaries provided especially
by medical specialists provides some insights that are immediately relevant to our
effort to develop a model for automatic summarization of medical articles. Firstly, professionals adopt a different strategy for the selection of summary relevant
content than language specialists, who can be assumed to be knowledgeable in
the structure of general discourse and linguistically motivated summary writing.
Secondly, all professionals tend to choose roughly the same content chunks for the
summary — which means that the summaries of the professionals can be drawn
upon for distillation of the summarization criteria. Thirdly, if available, the summary of the author (as one of the professionals) can be taken as reference for later
evaluation of our model.
In order to work out a summarization model for medical articles in Spanish,
we must thus draw upon the specialized discourse knowledge of the professionals,
i.e., study the summaries of the professionals.
4.2 Towards an extraction-based summarization model
The summaries of the medical specialists in Sections 4.1.2 and 4.1.3 reveal that:
–
–
–
–
the summary must include information from all four sections of the article;
numerical information from the Patients and Methods and Results sections
must be included;
definitions, historical data, confirmations of facts and references to previous
works should not be included into the summary;
the Discussion part of the summary must not be longer than the parts corresponding to the other sections of the article.
Obviously, such general criteria do not suffice for the construction of a fine-grained
summarization model. But they set up a framework which must then be refined.
Thus, we must ensure that the four sections are identified. Furthermore, we must
identify and assess numerical data (to be kept for the summary). This can be done
by searching for specific keywords, numerals, etc.
As a rule, numerical data are embedded into the discourse structure of the
text. Thus, in the following example (Example 1) from the Patients and methods
262 Iria da Cunha, Leo Wanner and Teresa Cabré
section, between the sentence containing numerical information and the succeeding sentence, the discourse relation ELABORATION in the sense of the Rhetorical
Structure Theory (cf. also Section 2.1) holds.11
Example 1: 12
[En marzo de 1997 se produjo, de forma explosiva, en una unidad de geriatría de
24 camas, un brote epidémico de colitis que afectó a 12 (50%) de los 24 pacientes
ingresados.]N [Se consideró que los pacientes estaban afectados por diarreas cuando
presentaban tres o más deposiciones diarias blandas o líquidas durante un mínimo
de 2 días, en ausencia de otra causa que lo justificara.]S
‘[In March 1997, in a geriatric unit of 24 beds, an epidemic outbreak of colitis, which affected 12 (50%) of the 24 admitted patients, occurred in an abrupt
form.]N [Due to the absence of another cause that justified it, it was considered
that the patients were infected by diarrhoea when they presented three or more
soft or liquid daily depositions during a minimum of 2 days]’S
In order to ensure that, on the one hand, the deletion of one element of a discourse
structure does not lead to incoherence, and, on the other hand, no summary relevant element of the discourse structure is deleted or no irrelevant element is kept
for the summary, the summarization strategy must draw upon the discourse structure.
As already mentioned in Section 2, in the literature, discourse driven techniques received a strong echo (Ono et al. 1994; Marcu 2000; Teufel and Moens
2002). However, it has also been noted that discourse criteria alone often lead to
both omission of summary relevant information and inclusion of summary irrelevant information. In the domain of medical articles, we found that in particular
discourse tree depth criteria used, e.g., by Marcu for elimination of text chunks,
are not reliable.13 On the other hand, when used independently of the discourse
tree depth, certain RST-relations (such as CONDITION and CAUSE, etc.) can be
well used on their own, without reference to other criteria. Consider the following
example:
Example 2:
[Disponer de un amplio arsenal de fármacos antihipertensivos puede constituir un inconveniente en la toma de decisiones terapéuticas]N [si los esquemas son
confusos.]S
‘[To have a wide arsenal of antihypertensive drugs can be an inconvenience for
making therapeutic decisions]N [if the schemas are confusing.]’S
Between the first and the second sentence, the RST relation CONDITION holds.
If the first sentence (the relation’s nucleus) is to be included into the summary,
the second sentence (the satellite) cannot be omitted.14 Other relations such as
Summarization of specialized discourse 263
ELABORATION, SEQUENCE, etc., require, as a rule, additional communicative
and/or syntactic criteria to be of use for summarization; for illustration, consider
Example 3.
Example 3:
[La infección por C. difficile es la causa más frecuente de diarrea nosocomial
en nuestro medio y representa el 15–20% de las diarreas asociadas al uso de
antibióticos.]N [El espectro clínico de la infección oscila desde la colitis seudomembranosa hasta la diarrea leve y el portador asintomático.]S
‘[The C. difficult infection is the most frequent cause of nosocomial diarrhoea
in our environment and represents the 15–20% of the diarrhoea associated to
the use of antibiotics.]N [The clinical spectrum of the infection oscillates between
the pseudo membranous colitis and the slight diarrhoea and the asymptomatic
bearer.]’S
In Example 3, between the first and the second sentence, the relation ELABORATION holds. If we know that the satellite sentence elaborates on the Theme of
the nucleus sentence,15 we can deduce that it provides further details on already
mentioned content and can thus be safely discarded from inclusion into the summary.
In addition to discourse, syntactic and communicative criteria, we must consider lexical summarization criteria. As already in the case of other types of criteria, lexical criteria include omission criteria and inclusion criteria. The latter are
based on the insight that for each thematic area, some cue words are available that
mark relevant text chunks. Our study revealed that in Spanish medical articles, the
set of the relevance-marking cue words includes the Spanish equivalents of such
nouns as
objective, object, summary, purpose, intention, result, etc.
and of such verbs as
[to] carry out, [to] associate, [to] analyze, [to] present, [to] relate, [to] evaluate, [to]
contribute, [to] study, [to] value, [to] find, etc.
Cf. two examples (the cue words are in bold):
Example 4:
El objetivo del presente estudio ha sido conocer el patrón nutricional de la población
escolar de un núcleo rural con marcado carácter industrial e identificar sus alteraciones nutricionales.
264 Iria da Cunha, Leo Wanner and Teresa Cabré
‘The objective of the present study has been to know the nutritional pattern of
the school population of a rural centre with a marked industrial character and to
identify its nutritional alterations.’
Example 5:
El estudio descriptivo analizó el estado de salud percibido según el tipo de trabajo
realizado (trabajadoras o amas de casa) y el resto de variables.
‘The descriptive study analyzed the state of health perceived according to the
working type carried out (workers or homemakers) and the rest of variables.’
From the empirical study, we can thus conclude that an adequate extraction-based
summarization model must be multidimensional. It must take into account textual, discourse, syntactic, communicative and lexical criteria.
5. Building a motivated summarization model
Our extraction model is a rule-based model that consists of four different submodules. Each submodule contains rules of an individual dimension or a combination of dimensions of the linguistic description: the textual, the lexical, the
discourse and the syntactic-communicative dimensions. To derive the initial rules
for each submodule, we carried out an empirical analysis of fifty medical articles
in Spanish. The submodules are continuously being extended as further material
is analyzed. Figure 7 shows the architecture of our model.
Let us briefly discuss each of the stages of our model.
5. Primary textual processing stage
The primary textual stage captures the four-partite textual structure of a medical
article: (1) Introduction, (2) Patients and methods, (3) Results, and (4) Discussion.
Remember that medical journals usually require the division of an article into four
sections, prescribing, roughly, the headings of each. Furthermore, they require a
summary to reflect the content of each section.
Given that the original articles contain predefined section titles, this stage is
nearly trivial — although we must take into account that deviations from the predefined titles may well be observed.
5.2 Primary lexical processing stage
The primary lexical processing stage eliminates text passages that are demonstrably not relevant to the abstract. These are, among others, passages that contain:
Summarization of specialized discourse 265
Figure 7. Architecture of the multidimensional summarization model
–
–
–
–
–
cue words referring to statistical evaluations of the content of the article;
references to tables and figures;
cue words identifying definitions (such as the noun definición ‘definition’ and
its adjectival and verbal derivatives) and explicit new term introductions;
references to previous and related work identified in terms of explicit bibliographical references and cue term patterns such as (DET/PRON +) trabajo(s)
‘work(s)’/estudio(s) ‘study(-ies)’/investigación(es) ‘investigation(s)’ / … (+
MODIF), with the optional DET/PRON including el ‘the’/otro ‘other’ /algún
‘any’ /ese ‘this’/… and the optional MODIF including previo ‘previous’/anterior ‘previous’/…;
historical data (dates, years, etc.).
5.3 Discourse + syntactic-communicative processing stages
The discourse and the syntactic-communicative processing stages are intertwined.
They adapt general purpose summarization criteria to the domain of medical
266 Iria da Cunha, Leo Wanner and Teresa Cabré
articles. As already mentioned above, we use RST, which proved to be suitable for
summarization, as discourse representation. However, our use of RST is rather
different from the uses discussed in the literature. Thus, while, e.g., Marcu (2000)
— see in particular endnote 13 — draws upon the depth of the discourse tree, we
assume that it is the nature of each RST-relation, which allows us to judge its importance for the summary. Cf. the following rule that makes use exclusively of the
discourse structure:16
IF S is satellite of a CONDITION relation CO
THEN KEEP S
It indicates that the satellite element must be kept in order not to loose essential
information. This rule is applicable to Example 2 above as well as to the following
Example 6.
Example 6:
Si los pacientes requieren un flujo superior a 3 litros por minuto, es probable que no
sea bien tolerado y seguramente la VAO no es la mejor solución.
‘If patients require a flow superior to 3 liters per minute, it is likely that it is not
well tolerated, and VAO is certainly not the best solution.’
Another rule of this type is:
IF S is satellite of a BACKGROUND relation B
THEN IF |SecSAT.rem| > 1 ELIMINATE S
(with SecSAT.rem as section in which the satellite S of B occurs)
Example 7:
[En la enfermedad isquémica coronaria (EIC) se han descrito diferentes alteraciones en la circulación sistémica del sistema hemostático, existiendo muy poca información de los posibles cambios que pueden acontecer en la circulación coronaria,
cerca de la lesión trombótica y sus diferencias con las alteraciones encontradas a
nivel periférico.]N [Nos planteamos este trabajo con el doble objetivo de investigar
si en los enfermos con EIC existen diferencias en distintas variables hemostáticas al
efectuar sus determinaciones en la sangre obtenida del seno coronario (SC) respecto
a la obtenida de la circulación periférica (CP), así como los posibles cambios que la
ATPC pudiera producir en la hemostasia en ambos lugares.]S
‘[In coronary ischemic disease (EIC), different alterations in the systemic circulation of the haemostatic system have been described, the information being scarce
of possible changes that may occur in coronary circulation, near the thrombosis
injury and its differences with alterations found at the peripheral level.]N [We address this work with the double objective of investigating whether there are differences in ill people with EIC with respect to different haemostatic variables when
Summarization of specialized discourse 267
carrying out their identification in blood obtained from coronary sinus (SC)
compared to blood obtained from peripheral circulation (CP), as well as possible
changes that ATPC could produce in the homeostasis of both locations.]S’
All RST-relations used in our model are summarized in Appendix 2 of this article.
As syntactic representation, we use the dependency-oriented deep-syntactic
structures of the Meaning-Text Theory, MTT (Mel’čuk 1988).17 As communicative
structure (i.e., information structure), we also use MTT’s communicative structure
(Mel’čuk 2001). As already pointed out above, syntactic-communicative criteria
and discourse criteria tend to be intertwined in that a syntactic or communicative
criterion is required to be valid for one of the elements (nucleus or satellite) of a
given discourse relation. Consider two examples:
IF S is satellite of an ELABORATION relation E
AND there is a syntactic dependency structure such that
‘X –ATTR→S’
THEN ELIMINATE S
(with ‘X’ as an element in the syntactic structure)
and
IF S is satellite of ELABORATION E
AND S elaborates on the Theme of the nucleus N of E
THEN ELIMINATE S
The application of both rules leads to the elimination of the S-fragment. The first
combines syntactic criteria with discourse criteria, while the second combines
communicative criteria with discourse criteria. Cf. an example for the first rule:
Example 8:
Curiosamente, en nuestro trabajo se comportó como factor de riesgo la edad tardía de la menarquia (incluyéndose esta variable en la población general y posmenopáusica, en el modelo final de riesgo).
‘Curiously, in our work, the late occurrence of the menarche acted as a risk factor
(this variable being included in the general and postmenopausal population, in
the final model of risk).’
(the APPEND-dependent is in bold)
The second rule applies to Example 3 above and to Example 9:
Example 9:
[A los portadores de cuerpos extraños intraabdominales que contienen cocaína,
con fines de contrabando, se les conoce con el síndrome del body packer.]N [Los prin-
268 Iria da Cunha, Leo Wanner and Teresa Cabré
cipales problemas médicos que se plantean en estos pacientes son: la sobredosificación de drogas por la rotura de uno de los paquetes y la obstrucción intestinal por
impactación de dichos paquetes en el tubo digestivo.]S
‘[Bearers of extrinsic intra-abdominal bodies containing cocaine with the purpose of smuggling are known as having the body packer syndrome.]N [The main
medical problems considered with respect to these patients are: overdose of drugs
due to the breakage of one of the packets and intestinal obstruction due to the
impact of these packets in the digestive tube.]S’
The combination of communicative structure criteria (and, in particular, of the
Theme/Rheme dichotomy) with discourse structure criteria turned out to be a
very reliable source of evidence for summarization. In the literature, both types of
structures have been applied so far separately (e.g., Marcu 2000; Teufel and Moens
2002) for the use of discourse structure.
5.4 Secondary textual and lexical processing stages
The secondary textual and lexical processing stages serve to assess the relevance
of the fragments of the article in question selected for inclusion in the summary.
The criteria that contribute to an increase of the relevance score of a sentence are
of two kinds:18
i.
occurrence of lexical units with specific characteristics: (1) verbs in the 1st
person form,19 (2) any content word from the title, (3) any lexical unit from
the list of cue words which has been compiled beforehand, (4) numerical information in Patient and methods and Results sections; cf. examples for each of
them:
(1) Nos hemos decidido a comunicar nuestra experiencia con la dieta hipocalórica
como tratamiento único en pacientes afectos de OSAS. ‘We have decided to
communicate our experience in the hypo caloric diet as unique treatment of
patients infected by BEARS.’
(2) Programas de detección precoz del cáncer de mama y acceso a la
mamografía en España. ‘Programs for the precocious detection of breast
cancer and access to mammography in Spain.’
(3) En las clases más altas (I-II), aunque la mujer trabajase exclusivamente en
el hogar, presentaba mejor estado de salud que las trabajadoras de las clases
más bajas. ‘In the highest classes (I- II), even if women worked exclusively
at home, they showed better health conditions than workers from lower
classes.’
Summarization of specialized discourse 269
(4) Separando la mortalidad atribuible por sexos, los resultados obtenidos fueron
del 21,2% en varones y del 10,0% en mujeres, mientras que los porcentajes
de mortalidad atribuible referidos a nivel nacional fueron del 20% y del 5%,
respectivamente para varones y mujeres. ‘Separating mortality with respect
to gender, the obtained results were of 21.2% in men and 10.0% in women,
whereas the percentages of mortality with respect to this criterion at national
level were of 20% and 5% for men and women, respectively.’
ii. the position of the sentence in the article: (1) the last paragraph of the Introduction section, (2) among the first two sentences of the Patients and methods
section; (3) among the first two sentences of the Results section; (4) the first or
the last paragraph of the Discussion section; cf. an example for case (2):
Los pacientes suicidas que padecían una enfermedad orgánica eran 45, lo que constituye un 17% del total. La edad media de estos pacientes fue de 58,3 años (varones
57,6 años y mujeres 59,2 años) con unos límites de 16 a 90 años. ‘Suicidal patients
who suffered an organic illness were 45, constituting the 17% of the total. The
average age of those patients was of 58.3 years (males 57.6 years and women 59.2
years) within the limits between 16 and 90 years.’
The last two stages, the secondary textual processing stage and the lexical processing stage, calculate the relevance of the individual sentences within the selected
fragments with respect to their inclusion into the summary; cf. a sample textual
rule and a sample lexical rule used during these stages:
IF sentence s is one of the 3 last sentences
of the “Introduction”
THEN ∆S := ∆S + δS
IF a sentence s contains a verbal form in 1st person plural
THEN ∆S := ∆S + δS
(with ∆S as the global relevance weight of the sentence s assessed so far and δS as
the increment of this weight in case the condition of the corresponding rule is
met).
The set of the summarization rules used in our model is given in Appendix 1.
6. Validity of the model
For the validation of the performance of our model in praxis, we compare the
outcome of its application to five medical articles arbitrarily chosen from our test
corpus with the author summaries and the summaries of three medical doctors.
The validation is done using two different techniques: ROUGE, which is a stan-
270 Iria da Cunha, Leo Wanner and Teresa Cabré
dard evaluation technique of summarization systems, and the Relative Euclidean
Distance.
6. Evaluation with ROUGE
For the ROUGE-based evaluation,20 we took the summaries of the authors and the
summaries of the three medical doctors as models. The summaries of our model
were the candidates to be evaluated. As baseline, we used a random selection of
the predefined number of sentences from each of the four sections of the original
article. The baseline thus incorporated some linguistic information also observed
by our summarization strategy; namely that the summary has to include content
from each section: Introduction, Patients and methods, Results and Discussion.
Given that ROUGE is set up for English and our material is in Spanish, we
had to adapt ROUGE for the task. More precisely, we had to (i) replace the English
stop word list by a Spanish list that contained the stop words relevant to our topic;
(ii) remove the Porter stemmer included in ROUGE, (iii) use lemmatized summaries.21
We carried out the evaluation with ROUGE 2 (which uses bigrams) and with
ROUGE SU-4 (which uses fourgrams). The results are shown in Tables 3 and 4.
The average of both measures is shown in Table 5.
Table 3. ROUGE 2 measures using as models the authors’ and medical doctors’ summaries and as candidates the summaries provided by our model
ROUGE 2
Our model
baseline
text 1
0.67719
0.5122
text 2
0.73856
0.20822
text 3
0.66617
0.30849
text 4
0.55205
0.17406
text 5
0.65112
0.28731
Table 4. ROUGE SU-4 measures using as models the authors’ and medical doctors’ summaries and as candidates the summaries provided by our model
ROUGE SU-4
Our model
baseline
text 1
0.64320
0.33333
text 2
0.72189
0.19590
text 3
0.63447
0.30432
text 4
0.52632
0.18221
Table 5. Average of ROUGE 2 and ROUGE SU-4 measures
ROUGE average
Our model
baseline
ROUGE 2
0.65702
0.29806
ROUGE SU-4
0.63213
0.26468
text 5
0.63476
0.30762
Summarization of specialized discourse 27
As the three tables show, the quality of the summaries of our model is rather high.
In the case of both, ROUGE 2 and ROUGE SU-4, it is considerably better than the
baseline.
6.2 Evaluation with the Relative Euclidean Distance
For the evaluation with the Relative Euclidean Distance, the results of the experiment are set out in Table 6. Table 6 has the same structure as Table 2. Thus, as
already in Table 2, the first column (Content) lists all statements that occur in any
of the summaries (i.e., either in our summary, in the summary of the author or in a
summary of one of the doctors); in total, the table contains 58 statements, of which
only six are shown. The second column (Variable) contains the identification label
of each statement. The third column displays the performance of our model. The
remaining columns (Author, Dr.1, Dr.2, Dr.3) specify whether the statement in
question has been included (‘1’) or not (‘0’) by the author or by one of the medical
professionals, respectively.
Table 6 shows that the medical professionals and our model equally “undersummarize” somewhat, i.e., include into the summary statements not included
by the author. However, in general, the model performs well. Also, we consider
it a positive sign that the summary relevance judgements of the medical doctors
largely coincide with the judgements of our model.
For a quantitative estimation of the similarity between the alternative summaries of the different subjects, we used the Euclidean distance between them, applying the Pythagoras theorem in the given 58-dimensional space (remember that we
are working with 58 statements / variables). Given that the absolute distance values are difficult to assess, we normalize the distances by the distance between two
maximally diverging vectors in our vector space: the one with 58 ‘1’s and the one
with 58 ‘0’s. The distance between these two vectors is 7.616. In Table 7, the distances are thus distances relative to 7.616. Note that to accommodate for the two
normalization vectors, Table 7 has been extended by two lines and two columns.
When reinterpreted in terms of the percentage of relative divergence (i.e., relatively to the maximal divergence), the figures in Table 7 mean that the five summaries of our model diverge from the author summaries in 41% of the cases, from
the summaries of Dr. 1 in 22%, and from the summaries of Dr. 2 and Dr. 3 in 26%.
In other words, the summaries of our model coincide with the summaries of the
authors in 59%, with the summaries of Dr. 1 in 74%, and with the summaries of
Dr. 2 and Dr. 3 in 78%. The author summaries and the summaries of the doctors
diverge in 34% and 32% of the cases, respectively. Consider a graphical representation of the distances in Figure 8.
272 Iria da Cunha, Leo Wanner and Teresa Cabré
Table 6. The inclusion of the statements from a sample article into the summary by the
different subjects and our model
Content
Estudiar los principales factores de riesgo
de infección por Clostridium difficile en una
unidad de geriatría.
Estudio de casos y controles retrospectivo.
El análisis multivariante confirmó la nutrición
enteral por sonda nasogástrica (OR = 6,73; IC
del 95%, 1,01–45,35) y los días de tratamiento
antibiótico (OR = 1,15; IC del 95%, 1,01–1,28)
como factores de riesgo independientes para
la infección por C. difficile.
El tratamiento antibiótico, el sondaje nasogástrico y las características de fragilidad de
este grupo de pacientes se asocian a la infección por C. difficile.
Se han implicado otros factores no relacionados con el tratamiento antibiótico como
factores de riesgo de infección por C. difficile.
La práctica de una política antibiótica restrictiva respecto a la utilización de antibióticos,
como se ha llevado a cabo en otros hospitales,
reducirá significativamente la colonización y
la CACD
…
Variable Model Author Dr. 1
Dr. 2
Dr. 3
X1–1
X1–2
1
1
1
1
1
1
1
1
1
1
X1–3
1
1
1
1
1
X1–4
1
1
1
1
1
X1–5
1
0
1
1
1
X1–6
…
1
…
0
…
1
…
1
…
1
…
Table 7. Distance (divergence) matrix
Relative Euclidean distance
1:Author 2:Model 3:Dr. 1
1:Author 0.000
3.162
2.646
2:Model
3.162
0.000
1.732
3:Dr. 1
2.646
1.732
0.000
4:Dr. 2
2.449
2.000
1.732
5:Dr. 3
2.449
2.000
1.732
6:0-vector 7.071
7.483
7.550
7:1-vector 2.828
1.414
1.000
Case
4:Dr. 2
2.449
2.000
1.732
0.000
2.000
7.483
1.414
5:Dr. 3
2.449
2.000
1.732
2.000
0.000
7.483
1.414
6:0-vector
7.071
7.483
7.550
7.483
7.483
0.000
7.616
7:1-vector
2.828
1.414
1.000
1.414
1.414
7.616
0.000
For deeper comprehension and further amelioration of our model, it is necessary to analyse cases where the summaries of our model do not coincide with the
author summaries. In the experiment discussed in Section 3.1, our model did not
include statements found in the author summaries under two concrete circum-
Summarization of specialized discourse 273
Figure 8. Distance between the summaries of the different subjects and our model (the
numbers in the first column refer to the subjects and our model as indicated in Table 4)
stances. Firstly, when the statements contained references to tables such as, e.g.,
“Tabla 2” ‘Table 2’, “Tablas 5 y 6” ‘Tables 5 and 6’. These statements are eliminated
in the primary lexical stage of our summarization.
In all other cases of divergence, our model “undersummarized” i.e., it selected
for the summary all statements also selected by the authors, and, in addition, some
others not chosen by the authors. These were, first of all, statements from the Discussion section, many of which have also been chosen by some or all of the three
doctors. Discussion is the most complex section of a medical article and thus the
most difficult one to summarize. The decision to include a statement from the
Discussion section into a summary is highly subjective. The fact that other medical
professionals tend to select the same statements as our model suggests that these
statements are sufficiently relevant to be included in the summary.
7.
Summary and conclusions
We believe that we provided evidence that a rule-based summarization model
that takes into account the whole range of partly domain-specific linguistic criteria (textual, lexical, discursive, syntactic and communicative) is able to provide
high quality summaries of medical articles. The implementation of this model, its
further extension and evaluation is subject of ongoing work. For the time being,
we are working with a sample corpus annotated semi-automatically with abstract
syntactic dependency structures, RST discourse structures and communicative
(Theme/Rheme) structures.22 We consider this a valid methodology for a “proofof-concept” realization of our model. The application of our model to unannotated
274 Iria da Cunha, Leo Wanner and Teresa Cabré
corpora would require the availability of a comprehensive parsing/analysis workbench for Spanish, which is still an open problem.
Acknowledgments
This work could not have been carried out without the valuable collaboration of the twelve
participants of the experiment. These are six linguists of the Institute for Applied Linguistics of
Pompeu Fabra University of Barcelona (Carme Bach, Anna Joan, Ricardo Guantiva, Rodrigo
Alarcón, Amor Muntaner and Rogelio Nazar) and six medical doctors from different institutions: Eduardo Barge (Juan Canalejo Hospital, A Coruña), Iria Glez (Teresa Herrera Hospital, A
Coruña), Daniel López (Juan Canalejo Hospital, A Coruña), Víctor Vicens (Bellvitge Hospital,
Hospitalet de Llobregat), María Giralt (Benito Menni Hospital, Sant Boi de Llobregat) and Mª
Josè Alierta (Spanish and Catalan Association of Psychoanalytic Psychotherapy, Barcelona). We
are grateful for their dedication and enthusiasm. Many thanks also to our expert in statistics
Jaume Llopis, who was of great help in the statistical evaluation part of our experiment.
Notes
. Extraction oriented summarization is based on the selection and subsequent potential reduction of summary relevant sentences — in contrast to “abstraction” oriented summarization,
which uses knowledge analysis and text generation techniques.
2. In accordance with the linguistic framework underlying our work, we use the term “communicative structure” for what is often referred to as “information structure” (e.g., Vallduví 1990;
Kruijff-Korbayová and Steedman 2003).
3. An overview of the state of the art on automatic summarization of general discourse is given
in Mani and Maybury (1999) and Mani (2001). Cf. also the introduction to the special issue on
summarization in Computational Linguistics (Radev et al. 2002) and also the forthcoming special issue on the topic in Information Processing and Management 43(4), 2007.
4. Farzindar et al. use the term “thematic structure” to refer to what is commonly known as
“text structure.” In order to avoid confusion, we use the term “text structure.”
5. Such surface criteria may well require deep processing — e.g., named entity recognition.
6. IMRD stands for Introduction, Patients and methods, Results and Discussion.
7. The 20 articles in our experiment are articles from Medicina Clínica, which forms part of the
Technical Corpus of the Institute for Applied Linguistics of the Pompeu Fabra University of Barcelona. This corpus is available online and can be accessed via http://bwananet.iula.upf.edu/.
8. Note that the purpose of this table is merely to illustrate the similarity of the content choices
made by the subjects, not to analyze why the individual subjects have picked one or the other
content chunk. Therefore, we do not include into the table such selection criteria as position of
the chunks in the text.
Summarization of specialized discourse 275
9. In the Diagram of Figure 3, the two first dimensions of the Multidimensional Scaling model
cover 67.9% of the information; in the Diagram of Figure 4, 61.9%. Taking into account that we
are working with 624 variables this means that there is a strong correlation among the variables
and that the representation obtained by Multidimensional Scaling is indeed reliable and informative.
0. In general, the subjects tended to select for the summary the maximal amount of chunks
permitted in the experiment (i.e., the 20 lines) while the summaries of some authors were considerably shorter.
. For the sake of the clarity of our presentation, let us mention that in RST discourse relations
are binary relations. Asymmetrical and symmetrical relations are distinguished. For instance,
ELABORATION, CONSEQUENCE, CAUSE, etc. are asymmetrical relations; LIST, CONTRAST, etc. are symmetrical relations. The head of an asymmetrical relations is called nucleus
(N), the tail — satellite (S). In symmetrical relations, both elements are nuclei.
2. The English translations of the following Spanish examples are very approximate and nearly
literal. Therefore some of them may appear on the edge of being ungrammatical.
3. In short, Marcu’s strategy prefers discourse structure elements that form part of nuclei of
discourse relations at several depths of the discourse tree. Satellite elements or elements that
form part of only a few (or one) nuclei are candidates for elimination.
4. In Marcu’s model, such a satellite would be a strong candidate for elimination.
5. We interpret the term Theme in the sense of the Prague school (Sgall et al. 1986) and of the
Meaning-Text Theory (Mel’čuk 2001); cf. also Section 5.
6. For better readability, the rules are cited in a pseudo-code format.
7. Dependency-based syntactic structures proved to be more suitable for the purpose of extraction-based summarization than constituency-based structures since the former provide us
with grammatical information whose relative relevance can be assessed (e.g., an attribute is less
relevant than an actant of a given lexeme; the first actant is more relevant than the second or
third actant, etc.), while the latter does not.
8. Each criterion that is met augments the relevance score of a fragment by a given weight
increment.
9. In medical articles, sentences containing 1st person verb forms are, as a rule, comments of
the authors on their own work — which makes them relevant for the summary.
20. We cannot go into details of the principles of ROUGE evaluation here. Interested readers
are asked to consult (Lin 2004).
2. Instead of using lemmatized summaries, we could have also used a Spanish stemmer. However, as has been shown in IR, for languages with rich inflection (such as Spanish), lemmatization is more appropriate.
22. The annotation of the corpus has been done by several annotators using an interactive annotation editor. Some of the annotators were linguists; others have been trained on the various
representation structures specifically for the annotation task. Contrary to the widespread opin-
276 Iria da Cunha, Leo Wanner and Teresa Cabré
ion that, for instance, RST-annotation is highly annotator-specific, we did not make this experience: the annotations by different annotators coincided to a large extent.
References
Abracos, J. and G.-P. Lopes. 1997. “Statistical methods for retrieving most significant paragraphs
in newspaper articles.” In Mani, I. and M. Maybury (eds.). Proceedings of the ACL/EACL’97.
Workshop on Intelligent Scalable Text Summarization. 51–57. Madrid, Spain.
Afantenos, S.D., V. Karkaletsis and P. Stamatopoulos. 2005. “Summarization of medical documents: A survey.” Artificial Intelligence in Medicine 33(2), 157–177.
Barzilay, R. and M. Elhadad. 1997. “Using lexical chains for text summarization.” In Mani, I. and
M. Maybury (eds.). Proceedings of the ACL/EACL’97. Workshop on Intelligent Scalable Text
Summarization. 10–17. Madrid, Spain.
Brandow, R., K. Mitze, and L. Rau. 1995. “Automatic condensation of electronic publications by
sentence selection.” Information Processing and Management 31, 675–685.
Ciapuscio, G. 1998. “Los resúmenes de la revista Medicina: Un enfoque diacrónico-contrastivo.”
Revista Signo y Seña 10, 217–243.
da Cunha, I. and L. Wanner. 2005. “Towards the automatic summarization of medical articles
in Spanish: Integration of textual, lexical, discursive and syntactic criteria.” In Proceedings
of the Workshop “Crossing Barriers in Text Summarization Research. RANLP-2005 (Recent
Advances in Natural Language Processing). 46–51. Borovets, Bulgaria.
da Cunha, I. and J. Llopis. In print. “Constatación de la validez de los resúmenes adjuntos a
artículos médicos de investigación de cara a la evaluación de resúmenes automáticos.” In
Proceedings of the XXIV Congreso Internacional de la Asociación Española de Lingüística
Aplicada. Universidad Nacional de Educación a Distancia, Madrid, Spain.
Damianos, L., S. Wohlever, G. Wilson, F. Reeder, T. McEntee, R. Kozierok, L. Hirschman and
D. Day. 2002. “Real users, real data, real problems: The MiTAP system for monitoring bio
events.” In Proceedings of the Conference on Unified Science & Technology for Reducing Biological Threats & Countering Terrorism. 167–177. University of New Mexico: Mexico.
Edmundson, H. P. 1969. “New methods in automatic extraction.” Journal of the Association for
Computing Machinery 16, 264–285.
Farzindar, A., G. Lapalme and J.-P. Desclés. 2004. “Résumé de textes juridiques par identification de leur structure thématique.” Traitement automatique des langues 45(1), 39–64.
Gaizauskas, R., P. Herring, M. Oakes, M. Beaulieu, P. Willett, H. Fowkes and A. Jonsson. 2001.
“Intelligent access to text: Integrating information extraction technology into text browsers.” In Proceedings of the Human Language Technology Conference (HLT 2001). 189–193.
San Diego, USA.
Goldstein, J., J. Carbonell, M. Kantrowitz and V. Mittal. 1999. “Summarizing text documents:
sentence selection and evaluation metrics”. In Proceedings of SIGIR-99. 121–128. Berkeley,
California.
Johnson, D.B., Q. Zou, J.D. Dionisio, V.Z. Liu and W.W. Chu. 2002. “Modeling medical content
for automated summarization.” In Annals of the New York Academy of Sciences. 247–258.
New York: New York Academy of Sciences.
Kan, M.Y., K.R. McKeown and J.L. Klavans. 2001. “Domain-specific informative and indicative
summarization for information retrieval.” In Proceedings of the Workshop on text summarization (DUC 2001). 19–26. New Orleans, USA.
Summarization of specialized discourse 277
Kan, M.Y. 2003. Automatic Text Summarization as Applied to Information Retrieval: Using indicative and informative summaries. PhD dissertation. Columbia University, New York, USA.
Kaplan, R.B., S. Cantor, C. Hagstrom, L.D. Kamhi-Stein, Y. Shiotani and C.B. Zimmerman.
1994. “On abstract writing.” Text 14(3), 401–426.
Kruijff-Korbayová, I. and M. Steedman. 2003. “Discourse and information structure.” Journal of
Logic, Language and Information 12, 249–259.
Kupiec, J., J. O. Pedersen and F. Chen. 1995. “A trainable document summarizer.” In Proceedings
of SIGIR-95. 68–73. New York, USA.
Lehmam, A. 1995. Le résumé des textes techniques et scientifiques, aspects linguistiques et computationnels. PhD dissertation. Université de Nancy 2.
Lenci, A., A. Água, R. Bartolini, S. Busemann, N. Calzolari, E. Cartier, K. Chevreau, and J. Coch
2002. “Multilingual summarization by integrating linguistic resources in the MLIS-MUSI
project.” In Proceedings of the Third International Conference on Language Resources and
Evaluation (LREC’02). 1464–1471. Las Palmas de Gran Canaria, Spain.
Lin, C. 2004. “Rouge: A package for automatic evaluation of summaries.” In Proceedings of the
Workshop on Text Summarization Branches Out (WAS 2004). 25–26. Barcelona, Spain.
Lin, C. and E. Hovy. 1997. “Identifying topics by position.” In Proceedings of ACL Applied Natural Language Processing Conference. 283–290. Washington, USA.
Luhn, H. P. 1958. “The automatic creation of literature abstracts.” IBM Journal of Research and
Development 2(2), 159–165.
Mani, I. 2001. Automatic Summarization. Amsterdam: John Benjamins.
Mani, I. and M. Maybury. 1999. Advances in Automatic Text Summarization. Cambridge: The
MIT Press.
Mann, W.C. and S.A. Thompson. 1988. “Rhetorical structure theory: Toward a functional theory
of text organization.” Text 8(3), 243–281.
Marcu, D. 2000. The Theory and Practice of Discourse Parsing and Summarization. Cambridge:
The MIT Press.
McKeown, K. and D. Radev. 1995. “Generating summaries of multiple news articles.” In Proceedings of ACM-SIGIR’95. 74–82. Seattle, USA.
Mel’čuk, I. 1988. Dependency Syntax: Theory and practice. Albany: SUNY Press.
Mel’čuk, I. 2001. Communicative Organization in Natural Language. The semantic-communicative structure of sentences. Amsterdam: John Benjamins.
Ono, K., K. Sumita and S. Miike. 1994. “Abstract generation based on rhetorical structure extraction.” In Proceedings of the International Conference on Computational Linguistics (ACL
94). 344–348. Kyoto, Japan.
Paice, C.D. 1990. “Constructing literature abstracts by computer: Techniques and prospects.”
Information Processing and Management 26(1), 171–186.
Pollock, J., and A. Zamora. 1975. “Automatic abstracting research at chemical abstracts service.”
Journal of Chemical Information and Computer Sciences 15(4), 226–232.
Radev, D., E. Hovy and K. McKeown. 2002. “Introduction to the special issue on Summarization.” Computational Linguistics 28(4), 399–408.
Riloff, E. 1993. “A corpus-based approach to domain-specific text summarisation: A proposal.”
In Endres-Niggemeyer, B. J. Hobbs and K. Sparck Jones (eds.). Proceedings of the Workshop
on Summarising Text for Intelligent Communication — Dagstuhl Seminar Report (9350).
13–17. Dagstuhl, Germany.
278 Iria da Cunha, Leo Wanner and Teresa Cabré
Saggion, H. and G. Lapalme. 2000. “Concept identification and presentation in the context of
technical text summarization.” In Proceedings of the ANLP/NAACL Workshop on Automatic
Summarization. 1–10. Seattle, USA.
Sgall, P., E. Hajičová and J. Panevová. 1986. The Meaning of the Sentence in its Semantic and
Pragmatic Aspects. Dordrecht/Prague: D. Reidel/Academia.
Silber, H. G. and K. F. McCoy. 2000. “Efficient text summarization using lexical chains.” Intelligent User Interfaces. 252–255.
Swales, J. (1990). Genre Analysis: English in Academic and Research Settings. Cambridge: Cambridge University Press.
Teufel, S. and M. Moens. 2002. “Summarizing scientific articles: Experiments with relevance and
rhetorical status.” Computational Linguistics 28(4), 409–445.
Toffler, A. 1970. Future Shock. New York: Random House.
Vallduví, E. 1990. The Information Component. PhD dissertation. University of Pennsylvania,
USA.
Authors’ addresses
Iria da Cunha
Institut Universitari de Linguistica Aplicada
(IULA)
Pompeu Fabra University
Pl. de la Mercè, 10–12
08002 Barcelona
Spain
M. Teresa Cabré
Institut Universitari de Linguistica Aplicada
(IULA)
Pompeu Fabra University
Pl. de la Mercè 10–12
08002 Barcelona
Spain
[email protected]
[email protected]
Leo Wanner
Institució Catalana de Recerca i Estudis
Avançats (ICREA) and Technology Department
Pompeu Fabra University
Passeig de Circumval·lació, 8
08003 Barcelona
Spain
[email protected]
About the authors
Iria da Cunha holds a Degree in Spanish Philology from the University of Santiago de Compostela, Spain, and a Masters in Applied Linguistics from the Pompeu Fabra University (UPF),
Spain. Currently, she is pursuing her PhD at the Institute for Applied Linguistics (IULA) of the
UPF in the area of automatic text summarization in specialized discourse.
Leo Wanner holds a Diploma in Computer Science from the University of Karlsruhe, Germany,
and a Doctorate in Linguistics from the University of the Saarland, Germany. After occupying
Summarization of specialized discourse 279
positions at the German National Centre for Computer Science, University of Stuttgart, University of Waterloo and the ISI, University of Southern California, he is currently an ICREA research professor at the Technology Department of the Pompeu Fabra University (UPF), Spain.
M. Teresa Cabré holds a Degree and a Doctorate in Philosophy and Letters from the Barcelona
University, Spain. After occupying positions at Barcelona University, the Catalan Terminology
Center, the Catalan’s Studies Institute and other institutions, she founded the Institute for Applied Linguistics (IULA) of the Pompeu University (UPF), of which she was the director from
1994 to 2004. She is Professor at UPF since 1994. Currently, she is the President of the Spanish
Association of Terminology.
Appendix 1: List of Extraction Rules
Given that the abstract writing instructions provided by the journal from which our articles
are taken requires the abstract to contain information from each section of the article, the rules
listed below apply to any of the sections — if not explicitly stated otherwise.
For the sake of a more compact and more transparent presentation of the rules, let us introduce
first a number of abbreviations:
∆s:
δs:
W:
SW:
T:
P:
Secs.rem:
SecSAT.rem:
SecNU.rem:
SL:
∆T:
global weight assigned to sentence s in the text.
weight increment added to/subtracted from ∆s during the application of an extraction rule
list of domain-specific prominent words
stop word list (words of domain-specific irrelevant words)
title of the article under summarization
list of prominent linguistic patterns that introduce summary irrelevant information
section in which the sentence s occurs
section in which the satellite S of a rhetorical relation occurs
section in which the nucleus N of a rhetorical relation occurs
the predefined length (in sentences) of the summary
predefined sentence weight threshold which a sentence needs to surpass in order
to be chosen for inclusion into the summary
A1. Textual Rules
IF sentence s is one of the 3 last sentences of the Introduction section
THEN ∆s := ∆s + δs
IF a sentence s is one of the 2 first sentences of the Patients and methods section
THEN ∆s := ∆s + δs
IF a sentence s is one of the 2 first sentences of the Results section
THEN ∆s := ∆s + δs
280 Iria da Cunha, Leo Wanner and Teresa Cabré
IF a sentence s is one of the 3 first sentences OR one of the 3 last sentences of the Discussion
section
THEN ∆s := ∆s + δs
A1.2. Lexical Rules
A1.2.1. Lexical Rules Scoring Sentences
IF for sentence s the following statement holds: ∃w: w ∈ W AND w ∈ s
THEN ∆s := ∆s + δs
IF for sentence s the following statement holds: ∃w: w ∈ T AND w ∈ s AND w ∉ SW
THEN ∆s := ∆s + δs
IF a sentence s contains a verbal form in 1st person plural
THEN ∆s := ∆s + δs
IF a sentence s contains any numerical information
AND s is in either in the Patients and methods section OR in the Results section
THEN ∆s := ∆s + δs
IF |Final summary| > SL
ELIMINATE Sentences SE from Final Summary with ∆SE < ∆T
A1.2.2. Lexical Rules Eliminating Sentences
IF a sentence s contains a reference r to numerical information
THEN IF r appears in parenthesis
THEN ELIMINATE r from s
ELSE IF |Secs.rem| > 1 ELIMINATE s from Secs.rem
IF a sentence s contains d (d being a definition or introduction) of a new concept c
THEN IF d appears in parentheses
THEN ELIMINATE d from s
ELSE IF |Secs.rem| > 1 ELIMINATE s from Secs.rem
IF a sentence s contains a reference r to a table or figure
THEN IF r appears in parenthesis
THEN ELIMINATE r
ELSE IF r is rendered in a linguistic pattern pP
THEN ELIMINATE p
ELSE IF |Secs.rem| > 1 ELIMINATE s from Secs.rem
IF a sentence s contains a reference r to previous or related work
THEN IF r appears in parenthesis
THEN ELIMINATE r
ELSE IF r is rendered in a linguistic pattern pP
THEN ELIMINATE p
ELSE IF |Secs.rem| > 1 ELIMINATE s from Secs.rem
IF a sentence s contains no verb
THEN IF |Secs.rem| > 1 ELIMINATE s from Secs.rem
Summarization of specialized discourse 28
A1.3. Discourse and Syntactic-Communicative Rules
A1.3.1. Discourse Rules
A1.3.1.1. Discourse Rules Eliminating Discourse Elements
A1.3.1.1.1. Discourse Rules Eliminating Satellites
IF S is satellite of a BACKGROUND relation B
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of a PURPOSE relation P
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of a JUSTIFICATION relation J
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of a RESULT relation RS
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of a CONCESSION relation C
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of REFORMULATION relation RF
THEN IF |SecSAT.rem| > 1 ELIMINATE S
IF S is satellite of a CIRCUMSTANCE relation CI
THEN IF |SecSAT.rem| > 1 ELIMINATE S
A1.3.1.1.2. Discourse Rules Eliminating Nucleus
IF N is nucleus of an INTERPRETATION relation I
THEN ELIMINATE N AND KEEP the satellite of I, S
IF N is nucleus of an EVIDENCE relation E
THEN ELIMINATE N AND KEEP the satellite of E, S
A1.3.1.2. Discourse Rules Keeping Discourse Elements
A1.3.1.2.1. Discourse Rules Keeping Satellites
IF S is satellite of a CONDITION relation CO
THEN KEEP S
IF S is satellite of a SUMMARIZATION relation SU
THEN KEEP S
A1.3.1.2.2. Discourse Rules Keeping Nucleus
IF N is nucleus of a CONTRAST relation CON
THEN KEEP N
IF N is nucleus of a UNION relation U
THEN KEEP N
IF N is nucleus of a LIST relation L
THEN KEEP N
282 Iria da Cunha, Leo Wanner and Teresa Cabré
IF N is nucleus of a SEQUENCE relation SE
THEN KEEP N
A1.3.2. Syntactic Rules
IF the syntactic dependency structure of a sentence s contains a subtree ‘X –APPEND→Y’
THEN ELIMINATE Y from s
A1.3.3. Discourse-Syntactic Rules
IF S is satellite of an ELABORATION relation E
AND there is a syntactic dependency structure such that ‘X –ATTR→S’
THEN ELIMINATE S
A1.3.3. Discourse-Communicative Rules
A1.3.3.1. Discourse-Communicative Rules Eliminating Elements
IF S is satellite of ELABORATION E
AND S elaborates on the Theme of the nucleus N of E
THEN ELIMINATE S
A1.3.3.2. Discourse-Communicative Rules Keeping Elements
IF S is satellite of ELABORATION E
AND S elaborates on the Rheme of the nucleus N of E
THEN KEEP S
Summarization of specialized discourse 283
Appendix 2: RST Relations used for summarization
Table A1.1: RST relations used in the summarization model
NUCLEUS-SATELLITE RELATIONS
MULTINUCLEAR RELATIONS
Type of relation
CONTRAST
JOINT
LIST
SEQUENCE
BACKGROUND
Example
[Los antecedentes de primer grado se relacionan con un mayor riesgo
de aparición del tumor,]N [mientras que los antecedentes familiares de
segundo grado no influyen de manera importante.]N
[The relatives of the first degree are related with a bigger risk of
appearance of the tumor,]N [whereas the family relatives of second
degree are not influential in an important way.]N
[En todos los pacientes se realizó un seguimiento radiológico]N [y
fueron dados de alta tras una radiografía del abdomen sin evidencia
de cuerpos extraños.]N
[A radiological monitoring of all patients was carried out]N [and
they were discharged from the hospital after an X-ray of the abdomen without evidence of unknown bodies.]N
[El 68% de los pacientes eran varones.]N [El 92% procedían de
Colombia.]N [El 65% ingirieron fármacos antidiarreicos.]N
[68% of the patients were male.]N [92% were from Colombia.]N
[65% took anti diarrhoea drugs.]N
[A todos ellos se les realizaron una historia clínica y un examen
físico.]N [Se les preguntó por el país de procedencia.]N [Se registraron
la frecuencia cardíaca, la temperatura y la presión arterial.]N
[A clinical case record was compiled and a physical examination of
all of them was carried out.]N [They were asked about the country
of origin.]N [The heart frequency, the temperature and the pressure
arterial were registered.]N
[A los portadores de cuerpos extraños intraabdominales que contienen
cocaína, con fines de contrabando, se les conoce con el síndrome del
body packer.]N [Hemos estudiado la aparición de complicaciones en el
seguimiento de individuos que ingieren estos paquetes de droga, con el
fin de poder dar unas normas de actuación en estos casos.]S
[Bearers of unknown bodies in the abdomen which contain cocaine
for the purpose of smuggling are known by the syndrome of the
body packer.]N [We have studied the occurrence of complications in
the treatment of individuals who ingest these packets of drugs with
the goal to be able to provide some norms of action in such cases.]S
284 Iria da Cunha, Leo Wanner and Teresa Cabré
CIRCUMSTANCE [Parece necesario propiciar algún tipo de campaña informativa para
sensibilizar a la población femenina ante el cáncer de mama,]N [mientras no se diluciden las incógnitas que plantean las costosas campañas
de detección temprana.]S
[It seems necessary to promote some kind of information campaign
to sensitize the female population with respect to breast cancer,]N
[as long as the unknown factors that the costly campaigns of early
detection imply are not clarified.]S
CONCESSION
[El porcentaje de curación fue algo menor en los obesos que en los
no obesos,]N [aunque esta diferencia no ha sido estadísticamente
significativa.]S
[The percentage of recovery was somewhat smaller in the case of
obese than in the case of no obese,]N [although this difference was
not statistically significant.]S
CONDITION
[Si los pacientes requieren un flujo superior a 3 litros por minuto,]S
[es probable que no sea bien tolerado y seguramente la VAO no es la
mejor solución.]N
[If patients require a flow superior to 3 liters per minute,]S [it is
likely that it is not well tolerated, and VAO is certainly not the best
solution.]N
ELABORATION
[La infección por C. difficile es la causa más frecuente de diarrea
nosocomial en nuestro medio y representa el 15–20% de las diarreas
asociadas al uso de antibióticos.]N [El espectro clínico de la infección
oscila desde la colitis seudomembranosa hasta la diarrea leve y el
portador asintomático.]S
[The infection by the problematic C. is in our view the most frequent cause of nosocomial diarrhoea and represents the 15–20% of
the diarrhoea associated with the use of antibiotics.]N [The clinical
spectrum of the infection oscillates from the pseudo membranous
colitis to the light diarrhoea and the asymptomatic bearer.]S
EVIDENCE
[Presentaron datos clínicos de obstrucción intestinal 11 pacientes.]N
[En todos ellos se observaron signos radiológicos de obstrucción.]S
[11 patients showed clinical data of intestinal obstruction.]N [In all
of them, radiological signs of obstruction were observed.]S
INTERPRETATION [La utilización de técnicas como el lavado gástrico, la endoscopia,
la extracción manual transanal o el uso de laxantes por vía rectal
para intentar extraer los paquetes aumenta el riesgo de rotura de los
mismos,]N [por lo que se desaconseja su uso.]S
[The aplication of techniques such as gastric cleansing, the endoscopy, the transanal manual extraction or rectal use of laxatives in order
to try to extract the packets increases the risk of their breaking,]N
[therefore their application is not advised.]S
Summarization of specialized discourse 285
JUSTIFICATION
PURPOSE
RESTATEMENT
RESULT
SUMMARY
[Se realizó cirugía en 7 pacientes (3.3%),]N [en cinco de ellos porque
presentaban obstrucción, en uno por rotura de uno de los paquetes y
en otro por ausencia de progresión de dos de los paquetes que eran de
tamaño superior al resto.]S
[Surgery was carried out to 7 patients (3.3%),]N [to five of them
because they presented obstruction, to one because of breaking of
one of the packets and to another because of the lack of progression
of two of the packets that were bigger than the others.]S
[Para que puedan cumplir su función con eficacia,]S [los SUH precisan
que exista un equilibrio apropiado entre la demanda asistencial y su
capacidad de respuesta.]N
[To enable them to comply with their function with efficiency,]S [the
SUH require that an appropriate balance between the demand of
assistance and their reaction capacity exists.]N
[Se han tenido en cuenta sólo los días efectivamente abstinentes;]N [es
decir, en caso de abandono del tratamiento, se considera que todo el
tiempo restante, hasta el día 180, se está consumiendo alcohol.]S
[Only really abstinent days have been taken into account;]N [that
is, in the case the treatment is given up, it is assumed that all the
remaining time, until the 180th day, alcohol is being consumed.]S
[Se practicó una radiografía simple del abdomen en todos los
enfermos.]N [Se observaron cuerpos extraños intra-abdominales en el
98,6% de los enfermos.]S
[A simple X-ray of the abdomen was performed on all patients.]N
[Unknown intra-abdominal bodies were observed in 98.6% of the
patients.]S
[Se realizó una radiografía simple.]N [También se llevó a cabo una
radiografía combinada mediante varias técnicas.]N [En resumen, se
han aplicado diferentes pruebas radiológicas.]S
[A simple X-ray was carried out.]N [A combined X-ray was also
carried out via several techniques.]N [In short, different radiological
tests have been applied.]S
Appendix 3: Sample Texts and Summaries
This Appendix contains the first and the second section of a medical article, the author abstract
and the abstract obtained by the application of the model proposed in this article.
Factores de riesgo de infección por Clostridium difficile en pacientes ancianos. Estudio de casos
y controles.
Fundamento.
La infección por C. difficile es la causa más frecuente de diarrea nosocomial en nuestro medio
y representa el 15–20% de las diarreas asociadas al uso de antibióticos. El espectro clínico de la
286 Iria da Cunha, Leo Wanner and Teresa Cabré
infección oscila desde la colitis seudomembranosa hasta la diarrea leve y el portador asintomático. La administración de antibióticos constituye el principal factor de riesgo y el más conocido,
aunque otros factores, como la toma de fármacos antiulcerosos o la gravedad de la enfermedad,
también han sido involucrados en esta infección.
C. difficile es un bacilo grampositivo anaerobio, productor de esporas, que puede colonizar a
pacientes hospitalizados, en especial a ancianos, tras tratamiento antibiótico. Entre el 7 y el
14% de estos pacientes son portadores del microorganismo, aunque raramente existe una producción suficiente de toxina A o B, exotoxinas responsables de las manifestaciones clínicas de
la enfermedad. Los pacientes ancianos, por sus especiales características (inmunosenescencia,
comorbilidad, fragilidad, ingresos hospitalarios frecuentes y polifarmacia) constituyen un colectivo predispuesto al desarrollo de esta infección.
El objetivo de este trabajo es estudiar los principales factores de riesgo asociados a la colitis por
C. difficile, en un colectivo de ancianos ingresados en una unidad geriátrica de agudos.
Pacientes y método.
En marzo de 1997 se produjo, de forma explosiva, en una unidad de geriatría de 24 camas, un
brote epidémico de colitis que afectó a 12 (50%) de los 24 pacientes ingresados. Se consideró que
los pacientes estaban afectados por diarreas cuando presentaban tres o más deposiciones diarias
blandas o líquidas durante un mínimo de 2 días, en ausencia de otra causa que lo justificara. En
todos los pacientes con diarrea se practicó la determinación de la toxina A de C. difficile por
el método de enzimoinmunoanálisis (ELFA, enzyme linked fluorescent assay, CDA2, VIDAS,
bioMèrieux). Por cada caso diagnosticado de colitis asociada a C. difficile (CACD) se recogieron
de forma retrospectiva 3 controles ingresados en la misma unidad, de igual edad (± 2 años)
y sexo y en la misma fecha (± 3 meses). Se cumplimentó un protocolo de recogida de datos a
partir de la revisión de historias clínicas. El estudio estadístico se realizó con el programa SPSS.
Para el estudio de las variables categóricas se aplicó la prueba de la *2 con la corrección de Yates,
o la prueba exacta de Fisher cuando los valores esperados en una o más casillas eran inferiores
a cinco. Para estudiar la asociación entre variables numéricas y categóricas se utilizó la prueba
de la t de Student o la prueba no paramétrica de la U de Mann-Whitney cuando las variables
no seguían una distribución normal. Se consideró que existía significación estadística con una
p < 0,05. Se realizó un análisis multivariante mediante un modelo de regresión logística paso a
paso que incluía las variables que resultaron significativas en el análisis univariado.
Autor abstract:
Fundamento: Estudiar los principales factores de riesgo de infección por Clostridium difficile en
una unidad de geriatría.
Pacientes y método: Estudio de casos y controles retrospectivo.
Our abstract:
Fundamento: El objetivo de este trabajo es estudiar los principales factores de riesgo asociados
a la colitis por C. difficile, en un colectivo de ancianos ingresados en una unidad geriátrica de
agudos.
Pacientes y método: En marzo de 1997 se produjo, de forma explosiva, en una unidad de geriatría de 24 camas, un brote epidémico de colitis que afectó a 12 (50%) de los 24 pacientes
ingresados. Por cada caso diagnosticado de colitis asociada a C. difficile (CACD) se recogieron
de forma retrospectiva 3 controles ingresados en la misma unidad.
Was this manual useful for you? yes no
Thank you for your participation!

* Your assessment is very important for improving the work of artificial intelligence, which forms the content of this project

Related manuals

Download PDF

advertisement