Evaluating Content Selection in Human- or Machine

Evaluating Content Selection in Human- or Machine
Evaluating Content Selection in Human- or
Machine-Generated Summaries:
The Pyramid Scoring Method
Rebecca J. Passonneau
Ani Nenkova
September 3, 2003
From the outset of automated generation of summaries, the diÆculty of evaluation has been widely discussed (e.g., [10] [25]). Despite many promising attempts, we believe it remains an unsolved problem. Here we present a method
for scoring the content of summaries of any length against a weighted inventory
of content units, which we refer to as a pyramid. Our method is derived from
empirical analysis of human-generated summaries, and provides an informative
metric for human or machine-generated summaries. It is particularly suited
for naturalistic summaries of multiple documents. By naturalistic summaries,
we mean human- or machine-generated summaries that select information from
source texts and re-present the information in concise, uent text.
What dierentiates the pyramid method from other approaches is that we
take the frequently made observation that there is no best model summary as
a foundation, rather than an obstacle. As we discuss later, we think the use of
multiple models, while preferable to a single gold standard, can only be an indirect way of approximating what a pyramid addresses directly. Previous work has
shown that despite the observed variations in content across human summaries,
certain information will be consistently selected from a source text ([3]). Our
inventory-based approach assigns dierential weights to content units, depending on their cognitive importance as reected by their frequency within a pool
of summaries. Given a pyramid inventory, there can exist multiple congurations of content units that would be assigned the same score. As a consequence,
our method predicts multiple, equally good summaries. Our score is calibrated
to observed distributions of content units in human summaries, rather than to
highly variable human scores (in contrast to the approach taken in [7]), because
it is diÆcult to elicit robust ratings from humans. To construct the pyramid,
we abstract from a repository of human-written summaries, although we believe this is only one method for constructing a pyramid. We believe alternative
methods, such as eliciting human judgments about the content in the source
texts ([22]), might lead to equally useful weighted inventories.
After presenting our method in idealized form, we illustrate its application to
three sets of human and machine summaries from DUC 2003. For the purpose
of comparison, we contrast our method with the DUC 2003 scoring procedure,
which also attempts to identify the underlying content units expressed in summaries. Our ultimate goal is to use the manual scoring presented here as a
foundation for developing an automated method, as noted in the conclusion.
However, the main purpose of this report is to present our scoring method, to
explain how we derived it from analysis of the content of multiple summaries
of the same document sets, and to demonstrate that the scores it leads to are
meaningful and robust.
In brief, the pyramid scoring method derives from the observation that dierent humans will summarize the same textual sources by selecting partly matching and partly distinct units of information. We explain here how we identify
dierent expressions of the same Summary Content Units (SCUs) within a set
of summaries. We weight SCUs according to their coverage, or, how many distinct summaries they appear in. In principle, the pyramid method depends on
a vertical rank-ordering of the disjoint coverage sets for a particular document
set, placing the largest set (lowest coverage) on the bottom and the smallest
(highest coverage) on top, as shown in Figure 5. Each tier in a pyramid corresponds to a weighting factor. Our empirical task is to populate the tiers of
a given pyramid with appropriately weighted SCUs. Figure 5, with four tiers,
illustrates an empirical fact about the distribution of SCUs: the greater the coverage associated with a tier, the fewer SCUs it will contain. In Figure 4, a Venn
diagram is overlaid on a pyramid to show it predicts multiple, equally relevant
summaries. Both gures are discussed below. At present, we use a pyramid
only to evaluate summary content: note that we treat each tier of SCUs as if it
were an unordered set; further, the only constraint on selection is the proportion
of highly weighted content units overall. These, of course, are simplications;
in future work, we hope to take into account ordering information and other
interdependencies among content units.
Our presentation is organized as follows. In Section 2, we briey discuss
related work in order to motivate the design principles of our method; in particular, the need to capture the variation seen in human summaries and the advantages of creating a weighted inventory of information, rather than relying on
a model summary, as in the DUC 2003 scoring method. In Section 3, we present
the formula with a limited number of weighting factors, then in Section 4 we
illustrate how we create an inventory of Summarization Content Units (SCUs)
to populate the tiers of a pyramid for scoring summaries of a particular document set. Section 5 gives a brief overview of the DUC 2003 scoring procedure.
In Section 6 we apply the pyramid method to the human-generated summaries
from DUC 2003, and in Section 7 we apply it to the machine-generated ones.
In both sections, we compare the scores we assign to the DUC 2003 summary
dataset with the DUC scoring method for both human- and machine-generated
summaries to argue that our method is more informative, more reliable, and
more robust. In Section 8, we discuss limitations and obstacles and our current
eorts to address them.
Related Work
Our main focus is on previous work on evaluation of naturalistic summaries,
but we also discuss evaluation of summaries produced by sentence extraction
in order to cover a variety of techniques. We argue that a prerequisite for
a consistent, informative, and robust metric is a better understanding of the
similarities and dierences across human summaries. In particular we contrast
pattern-matching evaluation methods, e.g., those that measure ngram overlap
of automated summaries with human-generated text (e.g., one or more model
summaries), with evaluation methods similar to the approach used in past and
current DUC eorts, based on human identication of overlap between abstract
content units (Model Units, or MUs; Elementary Discourse Units, or EDUs).
Because our method depends on creating a similarly abstract representation
of content units, we also discuss previous work on annotating such units with
respect to inter-annotator reliability.
Variation in Human Summaries and Summary Ratings
A major issue in the eld of automatic summarization is the observation that
when dierent humans produce summaries for the same document, each individual choses dierent information to include in a summary. Research as early
as [23] reports that extracts selected by six dierent human judges for 10 articles
from Scientic American had only 8% overlap on average (the 6 subjects agreed
upon an average of only 1.6 sentences per article out of 20 sentences selected
for each article). Also, judges agreed with their own previous judgments only
55% of the time. In their experiments measuring overlap was straightforward,
and also very conservative, since the entire sentence had to be an exact match.
The lack of overlap in the content of human summaries has been discussed
on numerous occasions (cf. [9], [24], [8], [7], [3]). Halteren and Teufel [3], for
example, collected 50 100-word summaries of the same 600-word text, and report
that no consensus emerges for the ideal content of a 100 word summary, nor
do the summaries fall into distinct homogenous groups. (We discuss their work
in more detail in Section 3.1.) Jing et al. [5] report that for single document
summarization good agreement can be achieved when the compression rate is
high, thus indicating that humans can consistantly point out the most important
information, but disagree on the ranking of less important fragments. This
conjecture was sustained by the fact that agreement deteriorated when the same
humans had to produce longer summaries.
It has often been suggested that any automatically assigned evaluation metric must be calibrated to a human evaluation [8] [7]. This approach has been
successful for evaluation of machine translation, notably with the Bleu [14] approach. However, it presupposes eliciting a reliable human evaluation, which
has proved problematic for summarization. For example, it has been observed
that human ratings of summaries are not very stable [7] [12], and we see this
in the current DUC 2003 results, as reported below (Section 6). Human ratings
of how important a given sentence is to a summary are sometimes inconsistent
for the same rater at dierent times [7] [19], although this might depend on the
specics of the task, given that results on a similar task appear more stable
in [21] [22].
For DUC 2003, a procedure was developed for eliciting human judgments
about the degree of semantic overlap between a so-called peer summary and a
model summary (see Section 5). Judges compare automatically identied Elementary Discourse Units (EDUs) in the model against sentences in the peer;
the comprehensive score for the summary is an average of the individual judgments for a given summary. As we illustrate in Section 6, the DUC 2003 results
are problematic in that dierent humans assign dierent scores to the same
summary, and summaries from the same human are judged to be dierent in
The reported variations across human summaries, and in human evaluation
of summaries, would seem to present a confusing, and ultimately discouraging, view of the feasibility of automated evaluation. We counter this view by
attempting to present a more coherent picture of the human summarization
process, and by demonstrating that given an appropriate annotation procedure
for content units, and an appropriate scoring method, humans can apply a
DUC-like evaluation of content that is both robust and meaningful.
Ngram Evaluation Applied to Summarization
There have been several attempts to apply the Bleu method [14] developed
for machine translation to summarization. In essence, the Bleu method counts
ngram overlap of a machine translation with a repository of model translations,
or with a translation of a single text, if it is suÆciently large. The scoring
in [14] applies a modied recall metric and a brevity penalty; most importantly,
its consistency with human evaluation has been demonstrated. Apparently, the
idea motivating the adaptation of this metric is that where translation maps
from one human language to another, summarization can also be viewed as a
translation of sorts: a more expansive text is translated (converted) to a less
expansive one. For translation, the limiting condition is that there are many
possible good translations of the same source language phrases. However, the
goal of a good translation is relatively well dened: to convey in the target
language LT a close approximation to what was conveyed in the source language
LS . In summarization, what counts as a good summary is not as well dened;
there are many more degrees of freedom with respect to selection of what to
say, how much to say, in what order, and to what rhetorical purpose.
In [19], the Bleu method is applied to the sentence extraction type of summarization. The authors note that there is low human agreement on ranking
sentences to include in an extract. They conclude from a series of experiments
that the Bleu method leads to consistent rankings of four systems only when
multiple reference summaries are used. A reference summary is constructed
from assessments of three judgments of sentences on a relevance scale of 1-10
(the same data as in [22]). We nd the results inconclusive, and diÆcult to
generalize to naturalistic summaries. Lin and Hovy [7], who also apply a pattern matching method analogous to Bleu, earlier reached the same conclusion
about the need for multiple reference summaries. In contrast to [19], they deal
with naturalistic summarization, and explicitly discuss the problematic issue of
lack of human agreement both across humans, and for the same human, on the
degree of semantic overlap of a summary sentence with a model one. Their solution is to compare the evaluation matrices produced by human and automated
evaluation; that is, they apparently assume that the variation in scores assigned
by humans is a valid target, rather than an artifact of a poor elicitation procedure or false assumptions about what humans understand the evaluation task
to be. They get a Spearman correlation coeÆcient of .70 using three reference
summaries on a multi-document task.
Annotation of Abstract Content Units
Previous work suggests that it is possible for humans to reliably annotate semantic units in text. For example, Halteren and Teufel [3] claim that factoid
identication is more objective than DUC-style judgments. Annotators proceed
by identifying similarities and dierences across texts, presumably a more objective judgment process than assigning a percentage to the degree of information
overlap between an EDU and a sentence. They report 96% recall and 97% precision of an individual annotator's representation, using a consensus annotation
as the standard. However, note that high recall and precision do not necessarily
correspond to high interrater reliability [16].
In preparing materials for assessing how the rhetorical and argumentative
characteristics of a text correlate with student readers' comprehension, Beck
et al. [2] apply a narrative analysis procedure originally developed by Omanson [13]. Their goal is to compare students' recall of original versus revised
passages, which means they need to quantify the overlap in content. Omanson's
original procedure identied content units at the clause level, but when applied
in [2], the authors found a need to create units often smaller than a clause (note
that we do as well in our SCU annotation). They report an interrater reliability
on a large sample of text coded by two raters of .92.
The high interrater reliability found by Beck et al. [2] and Halteren and
Teufel [3] led us to believe that a reliable procedure for coding SCUs was a
reasonable goal, despite the apparent lack of agreement on coding overlap of
content found in [7] and in the DUC 2003 data. It should be noted that the
narrative analysis method in [2] was developed and reported on over several
years. For other types of semantic annotation of discourse (e.g., dialogue),
it has also been shown that iteration over a set of instructions with explicit
procedures can yield good interrater reliability [4] [11].
Pyramid Scoring Method
The key observation that motivates the pyramid scoring method is that almost
all of the information contained in a human-written summary is contained in
the source texts,1 though expressed in dierent words, and that while no two
humans will write the same summary, the information in the source text can be
prioritized in terms of how likely it is to appear in any one person's summary.
Our goal is to develop a general method to abstract and prioritize units of information from source texts in a manner that replicates human behavior. This
will enhance our understanding of the human summarization process, and therefore lead to better metrics for evaluating automatically generated naturalistic
As noted in the previous section, Halteren and Teufel [3] discuss their collection of a large set of 100-word summaries of the same 600 word document
for which they analyzed the distribution of distinct content units they refer to
as factoids. Their Figure 1 (adapted here as Figure 1; each point on the X
axis represents the mean whereas their original gure indicates the range) plots
the growth in number of distinct factoids (Y axis) against the number of summaries, ranging from 1 to 40. They note that the number of distinct factoids in
a single summary ranges from 32 to 55, and that this number increases (apparently logarithmically) with the number of summaries examined. They conclude
that there is a dierence in the perceived importance between the various factoids, reected in the likelihood that a factoid occurs in multiple summaries.
We strongly concur, and believe that an evaluation metric for content selection should therefore assign a quantitative value to perceived importance, with
frequency across summaries being one (indirect) source of evidence. (In Section 6.2, we discuss alternative sources of evidence.) Here we use the DUC 2003
summaries in which four distinct human summaries were collected for thirty
document sets to illustrate the pyramid metric. Although this is relatively few
summaries per document set, it aords the opportunity to apply our SCU annotation and scoring to distinct document sets (in contrast to [3] which looks
at single document summarization for a single document).
While the DUC summaries, like the Halteren and Teufel summaries, are 100
words in length, they contrast with respect to compression rate and complexity.
The DUC 2003 task involved summarization from multiple source texts (N=10;
avg. document length = roughly 500 words, or 5,000 words per set). In the DUC
summaries, the degree of compression is thus far greater. Because the source text
consists of multiple documents, the selection task is more complex. It requires
the additional semantic task of integrating information across documents, which
presumably requires more inferencing (e.g., regarding sources of evidence), and
1 Halteren and Teufel [3] mention that rarely, a summarizer includes material based on
unjustied inference; in unpublished work on this topic, Richard Gerrig observed that with
semantically loaded material, summarizers are more or less likely to include unsupported
Factoid Growth in 40 Summaries
Number of Different Factoids
Number of Summaries
Figure 1: Halteren and Teufel's Figure 1 (slightly modied). The x axis gives
the average number of summaries, the y axis is the number of distinct factoids
in that number of summaries.
the additional pragmatic task of relating rhetorical structure across documents.
In lieu of factoid we use the term Summarization Content Unit (SCU; cf.
Section 2). In part, this is to avoid the danger that our interpretation of the
notion of factoid in [3] is imperfect, but more signicantly, it is motivated by our
belief that it is impossible to arrive at a single, best, complete and consistent
representation of a text in a formal semantics. For example, selecting the formal
predicates to represent natural language lexical items is necessarily somewhat
arbitrary, and inextricably related to the kinds of inferences the formal language
is intended to support (cf. [1] [18]). As noted in [3], although factoids are an
approximation of a rst order predicate logic style of semantics, the semantics of
a single factoid is motivated not by the inherent atomicity of the meaning, but
by the appearance of essentially the same content across multiple summaries.
They note that if two or more atomic propositions (eg., F1 and F2) are always
expressed together (e.g., in a single phrase), they treat them as a single factoid.
In order to address content selection, we must dene what we mean by a
Summarization Content Unit, or SCU. At present, we dene it indirectly in
terms of what we see in human summaries. In part, this is because we doubt
there can be an absolute denition that species what level of granularity of
concepts an annotator should use. Rather than attempting to provide a semantic
or functional characterisation of what an SCU is, our annotation procedure
denes how to compare summaries to locate the same or dierent SCUs. On the
one hand, we believe restricting annotation to such comparison leads to more
reliable results [6]; on the other hand, we also believe that natural language
semantics is exible, and we hope to develop a dynamic rather than static
representation of SCUs (similar in spirit to the notion of generativity in [20]).
Figure 2 presents one set of sample summaries from the DUC 2003 data
set. The topic pertains to a nancial crisis experienced by Philippine Airlines
(PAL). The sentences in the summaries have been numbered consecutively. As
Summary A
A.1 Philippine Airlines (PAL) experienced a crisis in 1998.
A.2 Unable to make payments on a $2.1 billion debt, it was faced by
a pilot's strike in June and the region's currency problems which reduced
passenger numbers and inated costs.
A.3 On September 23 PAL shut down after the ground crew union turned down
a settlement which it accepted two weeks later.
A.4 PAL resumed domestic ights on October 7 and international ights on October 26.
A.5 Resolution of the basic nancial problems was elusive, however, and as of
December 18 PAL was still $2.2 billion in debt and losing close to $1 million
a day.
Summary H
H.1 Starting in May 1998, Philippine Airlines (PAL) laid o 5000 of its 13,000
H.2 A 3-week pilots' strike in June and a currency crisis that reduced passenger
numbers made payments on PAL's $2 billion debt impossible.
H.3 President Estrada brokered an agreement to suspend collective bargaining for
10 years in exchange for 20% of PAL stock and union seats on its board.
H.4 The large ground crew union initially voted no.
H.5 After PAL shut down operations for 13 days starting Sept. 23rd, leaving much
of the country without air service and foreign carriers ying some domestic
routes, 61% voted yes.
H.6 Unions agreed to some employee cuts with separation benets.
Summary I
I.1 Philippines Airlines (PAL), Asia's oldest airline, devastated in 1998 by pilot
and ground worker strikes and with a rising $2.1 billion debt, stopped
all operations for 13 days.
I.2 A 10-year, no-strike agreement was nally ratied when the government began
using foreign carriers for domestic ights.
I.3 PAL resumed domestic ights on October 7 and international ights slowly
over the next weeks.
I.4 This shutdown had been another blow to the debt ridden national ag carrier.
I.5 Following the strike, eorts to revive the airlines met more obstacles.
I.6 Cathay Pacic Airways would only help if the payroll, including 200 pilots
hired during the pilot strike, were slashed.
Summary J
J.1 The fate of Asia's oldest airline, PAL, is uncertain.
J.2 Negotiations with Cathay Pacic Airways to infuse $100 million dollars into
the company collapsed when PAL Chairman Tan refused to agree to major
job cuts and to relinquish management control to Cathay.
J.3 PAL is buried under a $2.2 billion dollar debt it cannot repay and
$1 million a day losses.
J.4 A replacement investor is unlikely and President Estrada, though supported
by Tan in the election, has rejected a government bailout.
J.5 PAL's nancial troubles were exacerbated by a two-week shutdown in September due to a dispute with its largest union.
Figure 2: Phrases contributing to an SCU with Maximal Coverage (i.e., all 4
summaries; boldface)
Unable to make payments on a $2.1 billion debt,
made payments on PAL's $2 billion debt impossible
with a rising $2.1 billion debt,
PAL is buried under a $2.2 billion dollar debt it cannot repay
SCU 1:
SCU 2:
PAL has a debt of over $2 billion
PAL cannot make its debt payments
Figure 3: Candidate for one SCU with coverage 4 split into two with coverages
4 and 3
shown by the boldface lines in the gure, all four summaries mention that the
airline is over 2 billion dollars in debt (A.2, H.2, I.1 and J.3); in some cases it is
mentioned in the rst sentence, in other cases it is mentioned later. As noted in
the introduction, our scoring method does not deal with ordering issues, which
are undoubtedly important; here we address content selection only.
The four boldface phrases in Figure 2 illustrate a candidate SCU, namely
the PAL debt. However, note that three of the four summaries mention another
factor in connection with the debt: PAL's inability to repay it. Thus what rst
appears to be a single SCU with maximal coverage (4 summaries, cf. Section 1),
becomes two SCUs. Figure 3 illustrates the two SCUs, and gives the coverage.
Although we have not yet investigated the interrelation of the semantics of SCUs
and their coverage, it is probably not an accident that the SCU in Figure 3
with the lower coverage is semantically dependent on (presupposes) the higher
coverage SCU: that is, being unable to repay a debt (SCU 2) implies being in
debt (SCU 1).
In the next two subsections, we present the general formula for scoring summaries, then we illustrate a specic SCU annotation for one of the DUC sets of
human-generated summaries.
In our preliminary SCU annotations of DUC summaries, a 100 word summary
consists on average of 12 SCUs. In the process of rening our annotation
method, we nd we have moved towards a slightly higher number of SCUs,
thus the example pyramid included below in Figure 6 has 35 and the associated
summaries have 15 on average. In Halteren and Teufel's larger set of 40 summaries, they nd a range of 32 to 55 factoids per summary, and a total of 256
factoids overall. In considering the dierent ranges of factoids versus SCUs, it
is important to note that their number depends very much on the number of
summaries and what they contain. Any new summary can potentially yield a
candidate SCU having only partial identity with one already in the inventory;
this could result in the splitting of the original SCU into two new units, as
illustrated in Figure 3 above.
We believe that in principle, we could directly derive SCUs from a set of
Figure 4: Two of six ideally informative summaries of size 4
source texts, given a better understanding of how human readers and writers
select, prioritize, and rhetorically present linguistic meaning. Let us assume
for the sake of argument that we had a principled method of doing so that
yielded SCUs at a rate similar to our annotation, which we discuss in detail in
Section 4, say 30 SCUs per 100 words. At this rate, a set of ten source texts
consisting of 500 words each, as in the DUC 2003 sets, would yield 1500 SCUs.
With suÆcient data, such as a large pool of summaries (as in [3]), or relevance
judgments on each sentence (as in [21]), a pyramid would have many more tiers
than the four illustrated here.
It is an empirical question what the ideal pyramid size might be for a given
set of texts, assuming a very large number of summaries. As we discuss later, we
believe that the ranking of SCUs reected in our pyramids is only indirectly a
function of frequency within a summary pool, and that there are other methods
to determine how to prioritize the SCUs from a source text set. However, given
that we have four human summaries per document set from the DUC 2003 eort,
we illustrate our method using a 4-level pyramid, and present the equation for
computing scores based on this.
In essence, we score a summary by computing a ratio P of the observed
distribution of weighted SCUs found in the summary (D), divided by the ideal
distribution of weighted SCUs, or maximum possible score (M ax).
P = MDax
The idea behind this ratio is that a given summary will have an observed
number of SCUs, which is its size X , and the greater the proportion of maximally
weighted SCUs in X , the closer P will be to one. There can be multiple ideal
summaries for a given SCU size, as shown in Figure 4. Here we illustrate how
M ax and D are computed.
A pyramid has n tiers T consisting of disjoint sets of dierentially weighted
SCUs. Each tier Ti , where i ranges from 1 to n, has a cardinality jTij and
a weight wTi (corresponding to its observed coverage). If we index the tiers
bottom up, then in a pyramid with four tiers (as we will have for the DUC sets
we use here), the top tier is T4. Each tier provides a weighting factor equal to
its height, thus the highest T4 has a weight wT4 = 4, and so on down to T1
with wT1 = 1. In an optimal summary, the SCUs are distributed from the top
of the pyramid down until reaching the total number of SCUs to be expressed.
Thus the maximum possible score (M ax) for a summary of size X requires that
no SCUs from a given tier Tj can be selected until all the SCUs from the next
higher tier (Tj +1 ) have been exhausted. We multiply the number of SCUs from
a given tier by the weight. Thus, the maximum score for a summary whose size
X is equal to the size of the pyramid would have the ideal distribution shown
in (2).
Maxideal =
Xn w
jTi j
Where X is not equal to the size of the pyramid, the value of M ax depends on
calculating for each tier Ti how many SCUs the summary should contain, based
on the preference for more highly weighted SCUs:
Max =
Xn w
i=j +1
Xn jT j)
jTi j + wT (X
Xn jT j X )
where j = max(
i=j +1
In the equation above, j is equal to the index of the lowest tier a perfectly
informative summary will draw units from. This tier is the rst one top down
such that the sum of its cardinality and the cardinalities of tiers above it is
greater than or equal to the summary size. If X is less than the cardinality of
the most highly weighted tier, then M ax is simply X wTn (the product of X
and the highest weighting factor). If X is less than the total number of SCUs in
the top two tiers, then M ax is the sum of wTn jTnj and wTn 1 (X jTnj).
If X is greater than the total number of SCUs in the pyramid, then there are
various possibilities for weighting the SCUS that appear in the summary and
not the pyramid; we have not yet dealt with this situation.
Now that we see how M ax is computed, we can compare the ideal distribution predicted for a given summary with its observed distribution, D. We
determine the actual distribution of X into disjoint sets by nding the intersection Di of the summary SCUs with each tier Ti and computing the cardinality.
Then the pyramid score
Xn w
P is the ratio of D to M ax:
Figure 5: Distribution of SCUs in the pyramid inventory
P = Pn
i=j +1 wTi
Pni=1 wT Di
jTij + wT (X
Pni=j+1 jTij)
An ideal pyramid would represent all information in the source text, much of
which would have a very low weight (potentially zero). The more SCUs drawn
from a pyramid, the closer the new text is to a revision than to a summary. We
dene the pyramid in this manner in part because we don't believe there is a
single ideal size for a summary, and in part because we believe the line between
a long summary and a revision is blurred. For a summary whose size ts the
pyramid, the score P carries an implicit penalty for irrelevant information; SCUs
missing from the pyramid have weight zero.2
Because P compares the actual distribution of SCUs to an empirically determined ideal, it provides a more informative measure of whether the content of a
summary reects the way a sample of readers would prioritize the information
in the text, as reected in human summaries. We refer to the pyramid as an
idealization because we believe that what humans put into a summary is an
indirect reection of what they understand to be the relative importance of the
content of the source texts.
SCU Annotation
Figure 6 illustrates our consensus SCU annotation for the summaries in Figure 2.
As illustrated, a few SCUs appear in all 4 summaries (with weight w T4 = 4),
a larger number appear in 3 (wT3 = 3), and so on. The size of each tier in
the pyramid corresponding to this annotation is illustrated in Figure 5. Scores
assigned to specic summaries using this pyramid are deferred to Section 6.
One issue in developing an evaluation based on SCUs (or similar semantic
units) is reliability [6]. Is the identication of SCUs
2 One can imagine adding a negative weight for incorrect information, but although humans
occasionally do this, it rarely occurs in current automated methods.
wTi = 4 ( = 2: 12 PAs)
SCU-1: PAL has $2.1 billion debt (2 wT4 : 6 PAs)
SCU-2: PAL enforced a shutdown (2 wT4 : 6 PAs)
wT3 = 3 (N=6: 18 PAs)
SCU-3: PAL in crisis (1 wT4 /1 wT3 : 3 PAs)
SCU-4: PAL unable to repay debt (2 wT3 : 3 PAs)
SCU-5: PAL experienced pilots' strike (2 wT3 : 3 PAs)
SCU-6: this PAL crisis occurred in 1988 (1 wT3 /1 unsplit: 3 PAs)
SCU-7: shutdown began in September (1 wT3 /1 unsplit: 3 PAs)
SCU-8: shutdown lasted two weeks (2 unsplit: 3 PAs)
wT2 = 2 (N=12: 20 PAs)
SCU-9: to compensate for shutdown, foreign carriers ew domestic routes
(2 wT2 : 2 PAs)
SCU-10: PAL resumed domestic ights 10/07, international 2 weeks later
(2 wT2 : 2 PAs)
SCU-11: the pilot strikes were in June (2 wT2 : 2 PAs)
SCU-12: region experienced a currency crisis (2 wT2 : 2 PAs)
SCU-13: currency crisis resulted in reduced passenger numbers (2 wT2 : 2 PAs)
SCU-14: PAL is oldest Asian airline (1 unsplit/1 wT2 : 2 PAs)
SCU-15: PAL has losses of $1million/day (2 wT2 : 2 PAs)
SCU-16: PAL negotiated with Cathay for help (conict: 1 PA)
SCU-17: 10-year no-strike agreement struck (2 wT2 : 2 PAs)
SCU-18: ground crew union rst rejected settlement (1 wT3 /1wT2 : 2 PAs)
SCU-19: agreement accepted (1 wT3 /1 wT1 , 1 conict: 0 PAs)
SCU-20: in aftermath, PAL nances still shaky (1 wT2 /1 wT1 : 1 PA)
wT1 = 1 (N=15: 15 PAs)
SCU-21: [the strikes] inated costs. (2 wT1 : 1 PA)
SCU-22: as of May 1998, Philippine Airlines (PAL) laid o 5000 (2 wT1 : 1 PA)
SCU-23: 3-week [pilots' strike in June] (2 wT1 : 1 PA)
SCU-24: in exchange for 20% of PAL stock and union seats on its board
(2 wT1 : 1 PA)
SCU-25: much of the country without air service (2 wT1 : 1 PA)
SCU-26: [there were] ground worker [strikes] (2 wT1 : 1 PA)
SCU-27: 200 pilots hired during the pilot strike, (1 wT1 /1 unsplit: 1 PA)
SCU-28: as of December 18 PAL was still $2.2 billion in debt (1 wT1 /1 unsplit: 1
SCU-29: PAL asked CATHAY for $100 million dollars (2 wT1 : 1 PA)
SCU-30: negotiations re: relinquishing management control to Cathay (2 wT1 : 1
SCU-31: A replacement investor is unlikely (2 wT1 : 1 PA)
SCU-32: Tan supported Estrada in the election (1 wT1 /1 unsplit: 1 PA)
SCU-33: President Estrada has rejected a government bailout (1O/1 unsplit: 1
SCU-34: PAL's nancial troubles were exacerbated (1 wT1 /1 unsplit: 1 PA)
SCU-35: PAL Chairman Tan refused to agree to major job cuts
(1 wT1 /1 unsplit: 1 PA)
Figure 6: SCU Pyramid Generated from the Summaries in Figure 2
stable: will the same coder nd the same SCUs upon recoding the data
at a later time?
reproducible: will distinct coders nd the same SCUs?
accurate: is there a correct SCU analysis that any specic coding closely
Though we have not yet completed a full-scale reliability analysis, we are
condent that the method is stable and reproducible in principle, given our
procedure for annotating SCUs, our preliminary results, and claims made elsewhere that such coding can be done consistently by two or more researchers (cf.
Section 2.3). As noted above, we do not believe correctness applies.
To arrive at the consensus SCU annotation illustrated in Figure 6, the two
co-authors created independent SCU annotations, using a provisional set of
instructions. We achieved what we believe was rather good consistency, and we
expect this to improve as we rene the instructions. Our separate annotations
contained 33 versus 37 SCUs, and we created a consensus annotation consisting
of 35 SCUs. The symbols next to each consensus SCU represent a comparison
of the two original annotations:
2 wTi : both annotators agreed on the SCU, and on its weight (N = 21;
number M of all pairwise agreements=45)
1 wTi /1 wTj : both annotators agreed on the SCU, but diered on its
weight (N = 4); typically, the delta was one (N = 3)
1 unsplit: both annotators agreed on the weight of an SCU, and partially
on the SCU, but one found an additional SCU (N=10)
2 unsplit: both annotators agreed on the weight of a partial SCU, but
found a single SCU instead of two; the new SCU would necessarily be
assigned a lower weight (N=1)
conict: annotators diered on the semantics and membership of an SCU
To summarize our comparison of the two original annotations, we had roughly
the same number of SCUs, and in most cases assigned them the same weight.
We had perfect agreement on 23 SCUs: 2 of 2 where wT4 = 4, 2 of 6 where
wT3 = 3, 8 of 12 where wT2 = 2, 9 of 15 where wT1 = 1. Most disagreements
were not conicts on what counted as an SCU, but on whether to split an SCU.3
Other annotation dierences arose primarily from whether to create a singleton.
That is, almost all the dierences between the two annotators were due to how
3 For
example, SCU-6 and SCU-7 dierences were due to one annotator failing to split the
year and month from SCU-2; SCU-8 was not represented in the original annotations, but was
discovered during the consensus review and similarly resulted from a failure to separate out
the duration information mentioned in connection to SCU-2.
consensus SCUs
annot1 SCUs
annot2 SCUs
Table 1: Scores for the human summaries based on the two seperate annotations
versus the consensus annotation. No signicant dierence in scores is observed.
inclusive to make an SCU. There were only 2 conicts, i.e., cases where two
phrases were grouped together by one annotator, and each phrase was assigned
to a dierent SCU by the other annotator.
In sum, we nd complete consistency on SCU identication and weighting for
45 pairwise agreements (PAs), with 20 additional agreements where the annotators disagreed on weight (typically by one). Perfect agreement on the consensus
model would involve 69 pairwise agreements, compared to the observed 65. We
are currently considering how best to quantify the comparison of distinct SCU
One of the strongest arguments we can oer for the reliability of SCU annotation is that the two original annotations are similar enough not to aect
the scores signicantly. Table 1 gives three sets of pyramid scores for the PAL
summaries illustrated in Figure 2. The rst row gives the scores using the consensus annotation from Figure 6, and the next two rows give the scores for the
original annotations. In the next section, we explain how to interpret the scores;
here we note simply that there is no signicant dierence in the scores assigned
across the three SCU annotations (between subjects ANOVA=0.11, p=0.90).
DUC Scoring Method
Within the Document Understanding Conference, dierent aspects of summarization have been studied: the generation of abstracts and extracts of dierent
length varying between 50 and 400 words, single- and multi-document summaries, very short summaries and summaries focused by a topic or oriented by
opinion. The evaluation of summaries is based on the comparison of a summary (machine-generated, produced by a human or a baseline) by comparing
its content to a gold standard summary produced by a human, and called a
model. Over the years, dierent numbers of models were produced by NIST to
be used during the evaluation. In 2001, there were multiple models for some
document sets and some summary lengths used to study the dierent factors
that inuence a summary score. In 2002, there were two abstracts and two
extracts produced for each document set. In 2003 only generic summaries of
length 100 words were produced; NIST provided four human summaries, any
one of which could serve as the model. Thus in general, a model summary is
simply a summary produced by a human with no attempt to choose the best
model among the human summaries available. In 2003, a partial experiment
was performed on summaries focused by opinion to see if varying the model
summary produces dierent scores. The results for each system turned out to
be very close, regardless of which human summary was used as a model. No
attempt was ever made to use multiple model summaries for evaluation, but the
multiple human summaries for the 2003 docsets made our study possible.
The procedure used for evaluating summaries in DUC is the following:
1. A human subject reads the entire input set and creates a 100 word summary for it, called a model.
2. The model summary is split into content units, roughly equal to clauses or
elementary discourse units (EDUs). This step is performed automatically
using a tool for EDU annotation developed at ISI4 .
3. The summary to be evaluated (called a peer summary) is automatically
split into sentences. (Thus the content units of the model and the summary
to be evaluated are of dierent granularity|EDUs for the model, and
sentences for the peer).
4. Then a human judge evaluates the peer summary against the model. For
each content unit in the model:
(a) Find all peer units that express at least some facts from the model
unit and mark them.
(b) After all such peer units are marked, think about the whole set of
marked peer units and answer the question:
(c) \The marked peer units, taken together, express about k% of the
meaning expressed by the current model unit", where k can be equal
to 0, 20, 40, 60, 80 and 100.
The overall score for the summary is based on the content unit coverage.
In the ocial DUC results tables that NIST gives out, the score for the entire
summary is the average of the scores of all the content model units.Some participants use slightly modied versions of the coverage metric, where the proportion
of marked peer units to the number of model units is factored in.
The selection of units with the same content is facilitated by the use of the
Summary Evaluation Environment (SEE)5 developed at ISI, which displays the
model and peer summary side by side and allows the user to make selections by
using a mouse.
Application to human summaries
Here we illustrate the application of our scoring method to three sets of human
summaries from DUC 2003. In order to make a representative comparison that
4 http://www.isi.edu/licensed-sw/spade/
5 http://www.isi.edu/~
highlights the dierences between the DUC metric and the pyramid scores,
we selected sets assigned very high and very low scores by the DUC scoring
method. Our sample includes D30042, (referred to here as Lockerbie), which
had the highest average score in the DUC method, and the two sets that the
DUC method assigned the lowest scores, D31050 (China Democracy) and
D31041 (PAL|for Philippine Airlines). For the text of these summaries, see
Appendix A; note that the PAL set is also shown in Figure 2.
As with many other scoring methods, including the DUC method we compare our scores with, we believe a global score of summary content selection is
desirable in order to have a uniform metric of comparison across summaries,
whether written by humans or generated by machines. When we look at the
scores assigned by the DUC method to the three sets examined, here, we will review in more detail some of the drawbacks regarding the diÆculty in interpreting
the DUC scores. In brief, the drawbacks are that the method is asymmetrical,
in that scores depend on the choice of model summary to score against; scores
vary widely depending on factors other than the content contained in the summary, which hinders reliable conclusions based on the score.6 In addition, some
of the variablity we see with this scoring method is due to arbitrariness in the
decisions a scorer must make. In contrast to the DUC scoring method, we aim
for a symmetrical score, i.e., one which does not depend on the choice of a model;
one that quanties the content selection in a manner that supports meaningful
conclusions; and one that can be applied reliably by dierent evaluators. A
well-dened procedure for arriving at judgments on summary content will have
another advantage apart from reliability, namely a basis for determining the
feasibility of an automated method to perform a similar procedure.
Table 2 presents the DUC scores assigned to the three sets of summaries.
As described above, to apply this method requires selecting a model summary;
column 1 of the table gives the model for each set. Note that no metric can be
assigned to the model, so there is no way to determine whether one model (e.g.,
A for Lockerbie) is better than another (e.g., H for PAL). A second drawback
in choosing a designated model is that the resulting scores are asymmetric, that
is, the scores necessarily change depending on which summary is selected to be
the model.
In considering Table 2, consider the following additional problematic distributions. First, the variation across the high scoring set (Lockerbie) would
suggest, assuming a meaningful metric, that these four humans have extreme
variation in their summarization skills, with summarizer C being the least adept
summarizer (C has the Minimum score of .54), and summarizer B (with the
Maximum score of .82) being the most adept. In comparison, we see much less
variation within the other two sets in the table. Note also that summarizer D
seems to have produced a relatively good summary for Lockerbie and a relatively poor one for China, as if the same summarizer happened to vary widely
on dierent document sets.
6 For example, we suspect it has an undesirable sensitivity to the thematic similarity of the
source texts.
A (Model)
H (Model)
China Democracy
C (Model)
Table 2: DUC Scores for the Three Sets of Human Summaries
Std. Dev.
Table 3: Means DUC Scores and Deviations for 10 Human Summarizers
In fact, the variation for the three Lockerbie scores reects a general distributional inconsistency: among the 10 humans who participated in the summarization task, the individual and group variation is extremely high, and apparently random. There were a total of 90 human summaries scored on the
DUC method: the average score was .47, with a high standard deviation of .16
(Min=.10; Max=.82), meaning any individual's score is likely to be 33% higher
or lower than the average. Table 3 gives the averages and standard deviations
for each human; H had the highest average (Avg=.61; SDev=.14), and F the
lowest (Avg=.34; SDev=.18, i.e., any score by F is likely to be 50% higher or
lower than this average); most of the summarizers were close to the average but
showed spreads from 17% to 53% around the mean.
In Figure 7, a scatterplot of the scores of the 10 human summarizers by
summary, the summaries have been numbered from 1 to 30, with each human
scored on 9 of these summaries (where the 10th summary is the model). This
gure illustrates the striking lack of regularity in the way the scores vary: no two
humans have the same pattern of increases and decreases, and no two document
sets have the same ordering or grouping. Rather than assuming that the skills
of the human summarizers vary as extremely as these scores suggest, or change
from text set to text set independently of how other humans do on the same
Figure 7: Scatterplot of Human Summarizer Scores for DUC 2003 Human Summaries
text sets, we aim for a metric that is both more robust, meaning that any
dierences between humans are more stable, and that oers a more informative
There is a second problematic fact about the distribution in Table 2. The
scores on the Lockerbie set are much higher than the scores on the PAL and
China Democracy sets, which would suggest a qualitative dierence between
the two groups of summarizers, or between the two sets of source texts. However,
no obvious characteristic of the summaries distinguishes the apparently "poor"
summaries from the "good" ones.
There are many possible explanations for the disparity in the DUC scores on
the Lockerbie versus PAL and China Democracy summaries, but the one
addressed by the pyramid scoring method is that certain pieces of information
in a document set are going to be more relevant to more people, and should
thus be assigned a higher weight than less relevant information. Since the
DUC method treats all EDUs as having an equivalent weight, it is unable to
distinguish between summaries that have a high proportion of information that
a large sample humans would nd important.
In the following section, we will explain how the pyramid scoring method
eliminates two of the problematic distributional facts we see in the DUC scores,
inexplicably high variance in human performance, and inexplicably great variation between summary sets. Like the DUC method, we assign a single score
between 0 and 1 to a summary, based on the content, but our method avoids
the three weaknesses discussed above:
requirement for a designated "model" summary
equal weight assigned to all content units (EDUs in DUC)
insuÆcient reliability of the scoring method
Pyramid Scores of the Three Summary Sets
For the three sets of summaries discussed here, the two co-authors created a
consensus SCU annotation, labelling each SCU with a numeric index, a semantic
description, and the number of summaries the SCU appears in. Figure 6, for
example, gives the SCU annotation for the PAL set. The two raters (the coauthors) assigned 33 versus 37 SCUs, and arrived at a consensus annotation
with 35 SCUs for which we had 65 out of 69 pairwise agreements on the dual
attributes of SCU identity and rank.
Each annotation generates a pyramid, which is a rank-ordered partition of
SCUs. Table 4 presents the pyramid counts for the three sets of summaries.
Each column shows the distribution of SCUs across the four weights.
Table 5 presents the scores assigned by the pyramid method to the three sets
of summaries. Let's compare it with Table 2. First, note that because there is no
designated model summary, and we use the same inventory of SCUs for scoring
any summary, we can assign independent scores to all four. We have arranged
the table so that the rst column contains the summaries used as the model for
China Democracy
Table 4: Pyramids for the three sets of summaries
Table 5: Pyramid Scores for the Three Sets of Human Summaries
China Democracy
the DUC method. Second, note that the scores for the three sets are much more
comparable than the DUC method, and much higher. This suggests that our
methods treats summaries more equivalently, independent of which human has
written the summary, and somewhat independent of the source text (of course,
some texts may be inherently more coherent, and easier to summarize).
Most important for a meaningful evaluation metric, the pyramid scores can
be given a very specic interpretation. The highest scoring summary, summary
B for Lockerbie received a score of 1: this means that whatever the number
of SCUs in this summary, we know they are distributed from top to bottom
in the pyramid, with the higher ranking SCUs being fully represented before
any lower ranking SCUs appear. Similarly, the interpretation of the low scores
is that these summaries contain a lower proportion of high-ranking SCUs, and
a higher proportion of low-ranking SCUs. Furthermore, we can meaningfully
compare rows and columns of Table 5. Lockerbie summary C has the lowest
score, meaning it has a higher proportion of SCUs from the bottom of the
pyramid; in fact, it is equivalently weak in relevant content as China summary
A global score for content selection is desirable in order to compare systems,
but its utility is limited by the meaningfulness of the conclusions that can be
drawn. Our rst goal in developing a method to evaluate machine generated
summaries is thus to create a meaningful score for human summaries given the
observations noted in the preceding section, namely that summaries can vary
in length, that SCUs dier in their informational status with some being more
necessary to an adequate summary, and that SCUs are semantic abstractions
whose realization (and identication) in text exhibits a great deal of individual
variation, as well as being dependent on human cognition and on socio-cultural
assumptions. Because our long-term goal is to assign appropriate weights to
most of the information from the source texts, the pyramid metric can, in principle, score a summary of any length. Given an incomplete pyramid, a very long
summary containing information might be unfairly penalized (cf. [5], where it
is noted that length can aect results signicantly, which they cite as a problem
with designating a single model summary).
Second Order Summaries
If we could identify suitable features to assign to document sets and the SCUs
derived from them, it ought to be possible to discover generalizations about how
humans decide what content to include in a summary|given a sizable enough
training set|using statistical or machine learning techniques. However, it's
likely such a training set would need to be quite large, with a larger number
of summaries per source text as the compression rate and/or number of source
documents increases. For example, from Figure 1 [3], we see that the total
number of distinct factoids in a set of 100-word summaries of a single 600-word
document continues to grow steeply even after 10 summaries. In their data,
the compression rate is 1/6, from a single source text. This strongly suggests
that the DUC data, consisting of four 100-word summaries per document set,
where each document set consists of ten news articles of about 500 words each
(compression rate of 1=15), would not be an ideally large training set; still, it
might yield useful information about the characteristics of dierentially weighted
SCUs, and we hope to investigate whether this is possible.
Given the cost of creating a large training set, it would be extremely useful
to develop other less costly methods for collecting our pyramid data. Here we
present preliminary results of an alternative method we examined. We elicited
what we call second-order summaries from subjects, in which people were asked
to write 100-word summaries of DUC document sets after consulting only the
original 4 summaries, or the summaries plus source texts. Although this method
needs further development, it oers strong evidence that SCU frequency across
summaries is only an indirect reection of a more general cognitive process.
We take the distributional facts of SCUs within a sample of human-written
summaries of the same material to be an indirect reection of each individual's
linguistic and cognitive processes, and of the group's collective consciousness
as to which units of meaning in the documents are more important, more representative, or more useful.7 On this assumption, frequency of a given SCU
within a corpus of summaries of the same document is an artifact of a more
primary cognitive phenomenon that we might be able to study in other ways.
In [21], annotators ranked sentences within a document on a 10-point scale of
7 We also assume that one individual might write dierent summaries (cf. [25]), depending
on whether he or she understands the task to involve a presentation of a relatively objective
encapsulation of key information, or in terms of a more specic, and perhaps loaded, rhetorical
or information-seeking purpose.
DUC Score Pyramid Score
Lockerbie: Brief
M .40
N .58
P .67
Lockerbie: Full
M .58
Q .44
R .58
S .79
China Democracy: Brief
M .60
N .67
P .55
China Democracy: Full
M .38
Q .54
R .49
S .73
T .44
Table 6: Second Order Summaries: DUC versus Pyramid Evaluation
relevance to the topic of the document, and also assigned entailment relations
to sentences. Such relevance-ranking probably reects judgments similar to the
human process of content selection for summarization. In addition, the combination of sentence ranking and entailments in [21] resembles the decisions
annotators must make in creating an SCU annotation.
In the alternative source of evidence we discuss here, we asked humans to
write 100-word summaries from sets of DUC summaries. We had two conditions
for second order summaries, which we collected for the Lockerbie and China
Democracy sets. In the full condition, subjects read the summaries along
with the ten source texts. In this condition, the summaries presumably serve as
navigational aids in reading the source texts, as well as providing reinforcement
during the summarization process. In the brief condition, subjects read only the
original summaries. Table 6 compares DUC and pyramid scores on the second
order summaries. The DUC scores represent the average of the two authors'
scores. As we can see, the DUC scores were much higher for the second order
summaries than for the rst order ones, although the comparison is only valid
if the authors' execution of the DUC method was consistent with the DUC
evaluators. Again, the scores from the pyramid method are much higher than
the DUC method; they are not signicantly dierent from the pyramid scores
of the rst order summaries.
The pyramid scores for the brief condition appear higher than for the full
condition, although the small sample prevents us from placing too much emphasis on this observation. The pyramid average for brief is .89, and for full it is
.87. We expected lower scores on the full condition, because we expected that if
summarizers could consult the original texts, it was more likely that they would
select information that had not appeared in any other summary. This indeed
happened, with scores for the second order summaries dropping, but still they
remained in the range above .70. Though we did not have subjects write new,
original summaries (i.e, without access to the previously written summaries),
we believe such summaries would also receive reasonably high scores, given the
results under the full condition.
Some of the more interesting observations we nd in the second order summaries depend on qualitative analysis. For example, we found that occasionally,
an SCU would appear in all second stage summaries that had appeared in only
a single rst stage summary, which suggests it had accidentally low coverage in
the initial summaries.
Application to machine summaries
Machine generated summaries are rst broken down into clauses, that can be
divided further if a clause contains more than one SCU from the pyramid inventory. The total number of units obtained in this way is the SCU size of the
summary and is used to compute the score for an ideal summary, as described
by the scoring formula. The need to divide the machine summaries initially
into clauses comes from the fact that machine summaries very often contain
entire sentences that have no overlap with any SCU in the pyramid inventory.
The sentence could be kept as one unit, but then very long complex sentences
contain a lot of information. It is fairer to consider such sentences as contributing several SCUs to the summary size rather than a single one, given our SCU
annotation procedure, which directs annotators to split an SCU of weight 1 into
distinct SCUs of weight 1, if the SCU member has more than one propositional
constituent (see Appendix B)
Information is often unnecessarily repeated in machine generated summaries.
Figure 8 gives an example of the phenomenon: e.g., it contains the two phrases
hand over for trial two suspects and turn over for trial two other Libyans wanted
for . . . . So the next step is to identify units within the summary that contain
repeated information. Such units are combined and subsequently scored only
once. Among the 100 word generic machine-produced summaries submited for
DUC 2003, 30% contained unnecessarily repeated information (this gure can
be computed from the counts for quality question number 11, which explicitly
asked human evaluators reading the summary to say if there is any repeated
From here on the evaluation proceeds as in the case for human produced
summaries { each unit that is found in the pyramid inventory is given a score
as dened by the rank of the unit in the pyramid, and SCUs that do not appear
African countries voted in June to ignore the U.N. ight ban which was
imposed in 1992 to try and force Libya to hand over for trial two
suspects wanted in the 1988 bombing of an American airliner over
Lockerbie, Scotland. The reported jailing of the three oÆcials comes as
Gadha is under pressure to accept a plan to turn over for trial
two other Libyans wanted for the 1988 bombing of Pan am ight
103 over Lockerbie, Scotland, that led to 270 deaths. The visit was
Farrakhan's fth to Libya in the past three years. The leader of the U.S.based Nation of Islam most recently visited in December 1997.
Figure 8: System 19 summary for the Lockerbie docset.
DUC score
Pyramid Score
Table 7: Scores for machine summaries on the Lybia set
in the pyramid are given a score of zero. The overall weight of the summary is
computed and is then normalized by the weight of the M ax summary for the
specic size.
There is an immediately noticeable benet from the application of the pyramid evaluation method. It is more eective in discriminating between the summaries. Summaries that received the exact same score in the DUC evaluation,
received noticably dierent scores when scored with the pyramid method.
We examined closely pairs of such summaries, that were indistinguishable
according to the DUC evaluation, but markedly dierent according to the pyramid evaluation. Figure 9 shows two summaries that changed their ranking in
the two methods. Summary 17 and summary 6 for the PAL set had respective
DUC scores of 0.25 and 0.10 in DUC and respective pyramid scores of 0.26 and
If we look at the human summaries for the same set (PAL), we see that all
humans present the stories from the same angle: PAL has nancial troubles,
dierent factors make the troubles worse and the company is looking for ways
to get out of it. In this sense, summary 6 more closely resembles a human
summary; it states that the Philippine Airlines has a big debt and that strikes
Summary from system 6
PAL, Asia's oldest airline, has been unable to make payments on dlrs 2.1
billion in debt after being devastated by a pilots' strike and by Asia's currency
crisis. PAL earlier accepted a preliminary investment oer from Cathay
Pacic, Ailing Philippine Airlines and prospective investor Cathay Pacic
Airways have clashed over the laying o of PAL workers, prompting PAL
to revive talks with another foreign airline, an oÆcial said Tuesday. Cathay
Pacic Airways said Wednesday it had pulled out of talks to buy a stake in
ailing Philippine Airlines - making the uncertain future at PAL even cloudier.
Summary from system 16
President Joseph Estrada on Saturday urged militant unionists at Philippine
Airlines to accept a vote by workers approving a 10-year no-strike deal to
revive the debt-laden airline. President Joseph Estrada said Saturday the
nancially troubled Philippine Airlines will resume its international ights on
Sunday by ying him to Singapore where he will address the World Economic
Forum. Philippine Airlines said Thursday it will attempt to rebuild alone
after Hong Kong's Cathay Pacic Airlines pulled out of talks on acquiring a
stake in the ailing Philippine ag carrier. A strike by employees precipitated
the airline's near-death experience in September, when Tan shut down the
carrier after its unions re fused to accept a drastic cost-cutting plan.
Summary from system 17
Christmas is a sacred holiday in the Philippines, and nowhere is that more
evident than at the headquarters of Philippine Airlines. But Ramos, who
was intent on privatizing the economy, opened the industry to competition,
licensing rivals like Air Philippines, Cebu Pacic and Grand Air. PAL closed
for nearly two weeks on Sept. 23 after failing to persuade its largest union to
accept a management-proposed recovery plan under wh ich their collective
bargaining agreement would be suspended for 10 years in exchange for a
20 percent share of the company. The union had been sharply split over
the proposal, with militant members saying a suspension of the bargaining
agreement would violate workers' rights.
Figure 9: Three reranked summaries. The pyramid scores indicates system 6
produced a better summary and this corresponds to human judgment of the
content of the two summaries. Systems 17 and 16 got exactly the same score at
DUC, but 16 comes out better in the pyramid method.
sys 6
DUC score
Pyramid Score
Table 8: Scores for machine summaries on the PAL set
in the company and failed negotiations worsen the problems. Mentioning the
nacial troubles is semantically necessary in order to motivate and explain the
necessity for shutdown and the negotiations with other companies. Summary
17 fails to give such a context and regardless of the fact that it gives information
on the PAL shutdown and the bargaining over the recovery plan, the authors
feel it is less helpful than summary 6. This intuition is adequately captured by
the pyramid scoring method.
The dierence between summary 16 and 17 is not as big as between 17 and
6, but is still noticeable. Even though summary 16 has extraneous information
on President Estrada, phrases such as the nancially troubled Philippine airlines
and the airline's near-death experience in September convey enough of the main
topic of the docset. Summary 17 on the other hand is simply a collection of
disconnected facts with no main point or focus.
We have presented a method for representing the abstract content units of text
in order to create a weighted inventory of information content for evaluating
summarization. The most obvious conceptual limitation of the pyramid scoring
method is that we only address the issue of content selection. On the one hand,
we believe content selection is logically distinct from other issues in naturalistic
summarization, such as uency, coherence, interdependencies among content
units, rhetorical structure, perspective, and the possibility that sometimes the
inclusion of information can be more distracting than helpful (cf. [2]). We
hope we can tackle these other issues given our more informative and reliable
method for evaluating content selection. For example, in future work, we hope
to apply the same scoring method to question-answering, using the semantics
of the question to redistribute information within the pyramid.
The other primary drawback to the evaluation method proposed here that
we aim to address in subsequent work is that we have not yet determined how
feasible it will be to automate. There are two primary tasks that require automatic or semi-automatic methods:
populating a pyramid with SCUs, given a body of source text
scoring a summary against a given pyramid
We hope to test a variety of methods on each task. For example, it may be
possible to use machine learning methods to populate a pyramid from textual
sources, using an appropriate distance metric (e.g., a multi-dimensional one)
and clustering techniques, given a training corpus where humans have annotated
SCUs. We believe that a combination of deep (knowledge intensive) methods for
the exploratory phase followed by statistical and machine learning methods (as
in [17]) will establish a baseline and a clear path for performance improvements.
Three Sets of Human Summaries
Lockerbie Set (D30042)
A.1 In 1998 two Libyans indicted in 1991 for the Lockerbie bombing were
still in Libya.
A.2 In 1992 the U. N. voted sanctions against Libya for its refusal to turn
over the suspects.
A.3 Suering from the sanctions, Gadha alternated between deance and
A.4 In August of 1998 the U.N. proposed a trial in the Netherlands under
Scottish law which Libya accepted in principle, but insisted that
any sentence be served in the Netherlands or Libya.
A.5 The U.N. threatened a tightening of sanctions.
A.6 Then, in late November, the U. N. Secretary General hinted that he
might broker an agreement.
A.7 Optimism arose, but the issue was still in doubt.
B.1 Two Libyans were indicted in 1991 for blowing up a Pan Am jumbo jet
over Lockerbie, Scotland in 1988.
B.2 The United Nations imposed sanctions on Libya in 1992 because of
their refusal to surrender the suspects.
B.3 The sanctions included a ban on all international ights.
B.4 After six years of sanctions, Libyan leader Moammar Gadha, faced with
threats of additional sanctions, agreed in principle to hand over the suspects.
B.5 The two suspects would be tried in the Netherlands by Scottish
judges under Scottish law.
B.6 Gadha wants guarantees, including a promise that the suspects
would serve their sentences in the Netherlands or Libya if convicted.
C.1 Two Libyans, accused by the United States and Britain of bombing a
New York bound Pan Am jet over Lockerbie, Scotland in 1988, killing 270
people, for 10 years were harbored by Libya who claimed the suspects
could not get a fair trail in America or Britain.
C.2 The U.N. imposed international air travel sanctions on Libya to
force their extradition.
C.3 The sanctions were honored by all but certain African countries.
C.4 In 1998, a U.S.-Britain brokered compromise oered that their
trial to be held in the Netherlands.
C.5 Gadha, tired of sanctions and hoping for increased tourism,
actively pursued this compromise with UN Chief Ko Annan.
D.1 In December 1988 a Pan Am jet was blown up over Lockerbie, Scotland,
killing 270.
D.2 Two Libyan suspects were indicted in 1991.
D.3 Since 1992 Libya has been under U.N. sanctions in eect until the
suspects are turned over to United States or Britain.
D.4 Sanctions include an air embargo.
D.5 African leaders disregard them.
D.6 Libya has fostered tourism to help the damaged economy.
D.7 In August 1998 United States and Britain proposed a Netherlands
D.8 Libya agreed, but then asked for guarantees that the suspects,
if convicted, would be incarcerated in Libya.
D.9 This delayed progress.
D.10Ko Annan planned a December 1988 Libyan trip to move negotiations.
PAL Set (D31041)
A.1 Philippine Airlines (PAL) experienced a crisis in 1998.
A.2 Unable to make payments on a $2.1 billion debt, it was faced by
a pilot's strike in June and the region's currency problems which
reduced passenger numbers and inated costs.
A.3 On September 23 PAL shut down after the ground crew union turned
down a settlement which it accepted two weeks later.
A.4 PAL resumed domestic ights on October 7 and international
ights on October 26.
A.5 Resolution of the basic nancial problems was elusive, however,
and as of December 18 PAL was still $2.2 billion in debt and losing
close to $1 million a day.
H.1 Starting in May 1998, Philippine Airlines (PAL) laid o 5000 of
its 13,000 workers.
H.2 A 3-week pilots' strike in June and a currency crisis that reduced
passenger numbers made payments on PAL's $2 billion debt impossible.
H.3 President Estrada brokered an agreement to suspend collective
bargaining for 10 years in exchange for 20% of PAL stock and union
seats on its board.
H.4 The large ground crew union initially voted no.
H.5 After PAL shut down operations for 13 days starting Sept. 23rd,
leaving much of the country without air service and foreign carriers
ying some domestic routes, 61% voted yes.
H.6 Unions agreed to some employee cuts with separation benets.
I.1 Philippines Airlines (PAL), Asia's oldest airline, devastated in
1998 by pilot and ground worker strikes and with a rising $2.1
billion debt, stopped all operations for 13 days.
I.2 A 10-year, no-strike agreement was nally ratied when the
government began using foreign carriers for domestic ights.
I.3 PAL resumed domestic ights on October 7 and international ights
slowly over the next weeks.
I.4 This shutdown had been another blow to the debt ridden national
ag carrier.
I.5 Following the strike, eorts to revive the airlines met more
I.6 Cathay Pacic Airways would only help if the payroll, including
200 pilots hired during the pilot strike, were slashed.
J.1 The fate of Asia's oldest airline, PAL, is uncertain.
J.2 Negotiations with Cathay Pacic Airways to infuse $100 million
dollars into the company collapsed when PAL Chairman Tan refused to
agree to major job cuts and to relinquish management control to Cathay.
J.3 PAL is buried under a $2.2 billion dollar debt it cannot repay
and $1 million a day losses.
J.4 A replacement investor is unlikely and President Estrada, though
supported by Tan in the election, has rejected a government bailout.
J.5 PAL's nancial troubles were exacerbated by a two-week shutdown
in September due to a dispute with its largest union.
China Democracy Set (D31050)
C.1 Making obvious their intention to suppress the edging China
Democratic Party, Communist oÆcials arrested three of its most
prominent leaders, Xu Wenli, Qin Yongmin and Wang Youcai.
C.2 Colleagues protested and the USA expressed concern, to no avail.
C.3 China, defending its action, brought all three to trial.
C.4 Because potential defense lawyers were harassed, all three had to
defend themselves.
C.5 Qin and Wang were charged with inciting subversion, which carries
a minimum sentence of ve years imprisonment.
C.6 Xu faces the more serious charge of subverting state power which
can bring a 1life sentence.
C.7 Chinese President Jiang said multiparty democracy will not be
D.1 In October 1988 China signed the International Covenant on Civil
and Political Rights.
D.2 Perhaps emboldened by that, dissidents pushed for the recognition
of a new China Democratic Party.
D.3 The government, citing a long-standing policy, said that it would
not tolerate another political party.
D.4 Three prominent dissidents, one of them Wu Xenli, working to
establish the new party were arrested.
D.5 They were accused of endangering national security, and faced
prison terms of ve years.
D.6 Prospective defense lawyers were intimidated.
D.7 The United States criticized the arrests, and an embassy oÆcial
was rebued when he tried to observe a trial.
E.1 With the arrest of political dissidents, including Xu Wenli, Wei
Jingsheng, and Qin Yongman, China went on the oensive against
individuals seeking to set up political parties in that country.
E.2 In a speech to the Communist Party, Chinese President Jiang Zemin
denounced any attempts to set up a multi-party system in China.
E.3 The three prominent dissidents all were arrested on charges of
subverting national security.
E.4 With government harassment of lawyers, the dissidents have been
unable to secure legan counsel and have had to defend themselves.
E.5 The father of Qin Yongman has sought a delay in his son's trial
until legal counsel can be found.
F.1 Chinese oÆcials have nally spoken out on their dealings with
dissidents trying to establish the China Democracy Party.
F.2 They claimed that Xu Wenli, was suspected of "activities damaging
to national security".
F.3 The US has denounced China's detaining those "peacefully exercising
fundamental freedoms".
F.4 Those arrested are expected to be tried under China's vague State
Security Law.
F.5 Obtaining lawyers for their trials became virtually impossible
forcing some of the dissidents to defend themselves.
F.6 President Zemin said that economic reforms would continue but
would not be a prelude to multiparty democracy.
F.7 China did release ailing Liu Nianchun from prison work camp and
exiled him to the US.
Annotation of Summarization Content Units
The following procedure is a step-by-step process for identifying a set of SCUs,
along with their coverages (or weights), in a set of summaries of the same text.
It is intended to be used by knowledgeable annotators who understand the
summarization task, who have more than a passing familiarity with semantics,
and who are motivated to do a careful job. It requires patience, particularly
at the beginning: the initial decision steps can often seem unclear, and the
procedure requires multiple iterations over the evolving annotation. However,
as the task nears completion, the decisions become more clear cut, there is less
need to reconsider previous decisions, and the annotation moves more rapidly.
In principle, the task requires the human annotator to compare all the sentences
in all the summaries in order to locate dierent expressions of the same content;
we will exemplify what we mean by the same below. In essence, after locating
similar expressions, the annotator proposes an SCU, then records the phrase or
phrases that express it. During annotation, an SCU is considered provisional
until it is resolved, a conrmation step we describe below. As the annotation
proceeds, less and less of the original text needs to be examined to identify the
remaining SCUs. The annotation ends when no text remains to be considered,
and all SCUs have been resolved. The stepwise procedure makes use of two
work spaces: one to record the evolving SCUs, and one to record what remains
in the source text that has not yet been identied as expressing a particular
A software tool for editing that the annotator is comfortable with and that
allows for two distinct screens or windows. Much of the annotation task
involves copying or cutting text from one place and pasting it in another.
A le containing the set of summaries. We indicate how to preprocess the
summaries below.
Recommended: A printed copy of the summaries, after they have been
preprocessed as described below.
Recommended: A copy of the original text the summaries were created
from. This supports the preprocessing phase, and can also be useful to
resolve vagueness or ambiguity in the summaries.
B.3.1 Step 1: Preprocessing
Indexing. For all summaries S, assign an index to each summary. Then index
each sentence. In the examples below, we use the four summaries from DUC
2003 the China Democracy set (D31050) with the alphabetic indices they
were assigned (C through F), and here we use C1 through C7 for the seven
sentences in summary C, and so on. A convenient layout of the summaries is
one sentence per line, with the sentence index at the beginning of the line, as
in Appendix A.3.
Copy Editing. Summaries often contain misspellings or minor inconsisten-
cies, particularly of proper names, dates, and numbers. It can eliminate confusion to identify these in advance, particularly for annotators who have not read
the source texts, and who are not familiar with the content of the summaries.
For example, in the China Democracy summaries, a prominent Chinese dissident by the name of Xu Wenli is mentioned. In one summary, he was referred
to as Wu Xenli, which could lead to confusion. Thus we recommend altering:
D4 Three prominent dissidents, one of them Wu Xenli,
working to establish the new party were arrested.
D4 Three prominent dissidents, one of them Wu Xenli (sic; Xu Wenli),
working to establish the new party were arrested.
Similarly, in order to avoid the possibility that an annotator would identify
distinct SCUs on the basis of irrelevant dierences, these dierences can be
identied in advance. For example, the dollar amounts in the following examples
from the PAL summaries (D31041) could be marked in the text.
A2 Unable to make payments on a $2.1 (round to $2) billion debt, . . .
H2 . . . made payments on PAL's $2 billion debt impossible.
I1 . . . with a rising $2.1 (round to $2) billion debt, . . .
J3 PAL is buried under a $2.2 (round to $2) billion dollar debt . . .
B.3.2 Step 2: Create Workspaces
Workspace One: Record of Text Examined Put the summaries that
have been formatted one sentence per line into the rst workspace. This will be
used for two purposes:
1. to record what portion of each sentence has already been consumed, i.e.,
has been assigned to a specic SCU;
2. to keep track of the remaining text that needs to be examined
Workspace Two: Record of SCUs and Their Status Start a second
workspace in which to record the SCUs as they evolve. Each SCU will have:
1. a label, which may change as the task proceeds;
2. a set of phrases with indices indicating what sentence they came from;
3. a status indicator to record whether the SCU has been fully resolved.
B.3.3 Step 3: Propose an SCU
In this section we illustrate the rst proposed SCU in the China Democracy
summaries. This example will show that during the initial phase of an annotation, a proposed SCU is only provisional; it can change as the annotation
proceeds. Here, the proposed SCU1 was initiated with a phrase from sentence
C1, yet by the time we completed the annotation, no portion of C1 remained in
SCU1. We use this same example to show how, during Step 4, the process of
resolving an SCU can lead to splitting a provisional SCU into two provisional
SCUs. (SCU numbers are, of course, arbitrary; they merely provide identiers
for ease of reference.)
If you are just beginning: go to the rst sentence in Workspace One.
Place an open square bracket between the line index and the beginning of the
text, to indicate that the current sentence is being considered for the next SCU.
Insert SCU1 at the top of Workspace Two and copy the partly bracketed
sentence, along with its line index, from Workspace One into the space below
the SCU1 header you just created:
Workspace One
C1 [Making obvious their intention to suppress the edging China
Democratic Party, Communist oÆcials arrested three of its most
prominent leaders, Xu Wenli, Qin Yongmin and Wang Youcai.
C2 Colleagues protested and the USA expressed concern, to no avail.
... ...
Workspace Two
C1 [Making obvious their intention to suppress the edging China
Democratic Party, Communist oÆcials arrested three of its most
prominent leaders, Xu Wenli, Qin Yongmin and Wang Youcai.
Read the summaries to locate textual material in another sentence that has
some overlap in content with the sentence in the provisional SCU1 (e.g., C1).
Categories of overlap include (for textual examples, see below):
nearly the same propositional content in any two constituents, tensed or
same argument llers to a synonymous verb
same verb with argument llers that have an inferential relation as dened
in [15]
Here, as we read through the summary D sentence-by-sentence, the rst meaningful overlap occurs in D3, concerning the Chinese government's declaration
that it would not tolerate another political party, which is similar in content to
C1 their intention to suppress the edgling China Democratic Party. No other
sentence in D has more overlap with this material in the rst clause of C1; at this
point, three changes are made, one to Workspace One and two to Workspace
1. Workspace One: To record the consumed material from D3, enclose the
relevant phrase in D3 within brackets, labeling the rightmost bracket with
the SCU number;
2. Workspace Two: To identify the provisional phrase in C1 that contributes
to SCU1, add the right bracket indexed with the SCU number{]1{and
remove the rest of the sentence;
3. Workspace Two: To record the growth of SCU1, insert a copy of the
bracketed phrase from D3 in Workspace Two. Note that the referent of
the NP The government in D3 is the rst argument of both the main
clause verb to say and the gerund citing. For the sake of clarity, we show
D3 below with the subject of say and omit the parenthetical gerundive
The workspaces should now look as follows:
Workspace One
C1 [Making obvious their intention to suppress the edging China
Democratic Party,]1 Communist oÆcials arrested three of its most
prominent leaders, Xu Wenli, Qin Yongmin and Wang Youcai.
C2 Colleagues protested and the USA expressed concern, to no avail.
... ...
D3 The government, citing a long-standing policy, [said that it would
not tolerate another political party.]1
Workspace Two
C1 [Making obvious their intention to suppress the edging China
Democratic Party,]1
D3 [fthe governmentgsaid that it would not tolerate
another political party.]1
The three steps shown above establishes that the content of SCU1 will have
something to do with the overlap pertaining to a negative attitude of the Chinese
government towards an opposition party. The addition of material from D3 has
been added to SCU1 in Workspace Two, and Workspace One has a record
of this same material being consumed in the evolution of SCU1.
As we proceed in this fashion linearly through the next two summaries, E and
F, we reach a point where we have material from all four summaries associated
with the provisional SCU1, as illustrated below:
Workspace Two
C1 [Making obvious their intention to suppress the edging China
Democratic Party,]1
D3 [fthe governmentgsaid that it would not tolerate
another political party.]1
E3 [fPresident Zeming denounced any attempts to set up a
multi-party system in China.]1
F6 [fPresident Zeming said . . . fCLAUSEg would not be a prelude
to multiparty democracy.]1
B.3.4 Step 4: Attempt to Resolve the Proposed SCU
Resolution of an SCU involves adding a label that expresses its content, and
checking the consistency of the covered phrases with that content. Any material
expressed in a phrase from one of the summaries that is inconsistent with the
SCU label, or that has too much additional content in comparison to the other
phrases, should either be removed from the brackets in both workspaces, or
should be moved to a new SCU in Workspace Two. First we illustrate resolution
by example. Then we list the criteria used in the resolution phase.
When we check the contents of the provisional SCU1, we nd that the phrases
from D3, E3 and F6 are more like each other than they are to C1. For these, we
created the label Chinese government or governmental representative speaks out
against multiparty system (cf. new version of Workspace Two below). To the
right of the label, we add a question mark in square brackets to indicate that the
SCU is not yet fully resolved. When we do a second pass over the summaries,
we nd another sentence in summary C, namely C7, that is closer to the current
SCU1 than C1 is. For the moment, we move C1 to a new provisional SCU, and
add C7 to SCU1:
Workspace Two
SCU1 Chinese government or governmental representative
speaks out against a multiparty system [?]
D3 [fthe governmentgsaid that it would not tolerate
another political party.]1
E3 [fPresident Zeming denounced any attempts to set up a
multi-party system in China.]1
F6 [fPresident Zeming said . . . fCLAUSEg would not be a prelude
to multiparty democracy.]1
C7 [Chinese President Jiang (sic; Zemin) said
multiparty democracy will not be allowed.]1
Note that lines C7 and C1 in Workspace One should also be updated so that
the correct phrases are bracketed, and have the correct indices (not shown here).
We can attempt to fully resolve SCU1 now, or defer it to the nal annotation
step (Step Five). To do so now, we re-check the consistency of the phrases with
the label for SCU; if they are consistent, we can remove the bracketed question
mark. In this case, three of the summaries have President Zemin as the voice
that speaks out against a multiparty system, while the fourth refers only to
the government. To insure the internal consistency of SCU1, we move the three
references to the Chinese president to a new SCU (SCU3). Skipping some of the
details here, we label the new SCU3, check its consistency, and stop when we
have a fully resolved SCU1 and SCU3, and a provisional SCU2. Here we show
the weights assigned to the resolved SCUs (SCU1 and SCU3). We show SCU2
incremented with a phrase from E1, but we do not document the completion of
SCU2 here.
Workspace Two
SCU1 (w=4): Chinese government or governmental representative
speaks out against a multiparty system [?]
D3 [fthe government...g said that it would not tolerate
another political party.]1
E2 [f a governmental authorityg denounced any attempts to set up a
multi-party system in China.]1
F6 [fa governmental authorityg said... would not be a prelude
to multiparty democracy.]1
C7 [fa governmental authorityg said
multiparty democracy will not be allowed.]1
C1 [Making obvious their intention to suppress the edgling
China Democratic Party,]2
E1 [China went on the oensive against...
findividuals seeking to set up political partiesg]2
SCU3 (w=3): The voice of governmental authority (SCU1) is President Zemin
E2 [Chinese President Jiang Zemin f....denounced....multi-party systemg]3
F6 [President Zemin . . . fsaid . . . would not be a prelude . . . g]3
C7 [Chinese President Jiang fsaid multiparty . . . not . . . allowedg]3
Criteria for Resolution Step
1. preserve or prefer SCUs with greater coverage; that is, in considering different ways to split a provisional SCU during the resolution step, try to
maximize coverage, as long as the semantic decisions remain commensurate
2. SCU members can have minor semantic dierences. In the following example of an SCU from the China Democracy annotation, note that
C4 has potential where D6 has prospective and E4 has no corresponding
adjective; C4 and D6 refer to defense lawyers while E4 refers simply to
lawyers; in C4 and E4, harrass is the lexical stem of the action taken
against the lawyers whereas in D6 it is intimidate: SCU9 (w=3): arrested dissidents' lawyers were harrassed
C4 [Because potential defense lawyers were harassed,]9
D6 [Prospective defense lawyers were intimidated.]9
E4 [With government harassment of lawyers,]9
3. for an SCU of coverage X that seems internally inconsistent because there
is non-overlapping content of a more signicant nature than in item 2,
then consider creating two SCUs, one with coverage X and one with a
coverage less than X so long as resulting SCUs are either resolved, or are
closer to being resolved (cf. the creation of SCU3 above)
4. for any singleton SCUs, split propositionally complex sentences or clauses
into distinct SCUs (e.g., conjoined clauses, relative clauses, parenthetical
clauses, innitive clauses or phrases, gerundive phrases with one or more
argument llers)
5. when considering alternative ways to resolve an SCU, prefer a resolution
that avoids singletons, as long as criteria 1 through 4 are adhered to
6. allow for SCUs based on inferred content if
this will lead to a bigger coverage SCU, and
this will lead to resolution of otherwise unresolved SCUs, and
you are condent that the writer intends the inference, given the rest
of the summary (For example, we added F4 to an SCU about Xu
Wenli's arrest because we inferred that the writer implicitly referring
to the arrest; referring to Appendix A.3, however, you can see that
the writer never explicitly mentions the arrest of Xu Wenli)
7. when there are apparent contradictions among the phrases within an SCU,
assume the information is incomplete but consistent if there is a way to do
so (e.g., the summaries discuss the charges brought against the dissidents
as endangerment of national security or subverting state; whether these are
considered to be the same charge may depend on more detailed knowledge
than is available about Chinese law
B.3.5 Step 5: Resolve all Unresolved SCUs
Steps 3 and 4 should be repeated until all textual material in all sentences
has been identied as contributing to a specic SCU, and recorded as such
by enclosing relevant portions of text within brackets labelled with the SCU
number in Workspace One, and by copying the bracketed phrases to the
relevant SCUs in Workspace Two. When all the text has been so labelled,
there might remain some unresolved SCUs. At this point, apply Step 4 to the
remaining SCUs, using the criteria listed above.
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