Exploiting Timelines to Enhance Multi-document Summarization

Exploiting Timelines to Enhance Multi-document Summarization
Exploiting Timelines to Enhance Multi-document Summarization
Jun-Ping Ng1,2 , Yan Chen3 , Min-Yen Kan2,4 , Zhoujun Li3
DSO National Laboratories, Singapore
School of Computing, National University of Singapore, Singapore
State Key Laboratory of Software Development Environment, Beihang University, China
Interactive and Digital Media Institute, National University of Singapore, Singapore
[email protected]
We study the use of temporal information
in the form of timelines to enhance multidocument summarization. We employ a
fully automated temporal processing system to generate a timeline for each input document. We derive three features
from these timelines, and show that their
use in supervised summarization lead to a
significant 4.1% improvement in ROUGE
performance over a state-of-the-art baseline. In addition, we propose T IME MMR,
a modification to Maximal Marginal Relevance that promotes temporal diversity
by way of computing time span similarity, and show its utility in summarizing
certain document sets. We also propose a
filtering metric to discard noisy timelines
generated by our automatic processes, to
purify the timeline input for summarization. By selectively using timelines guided
by filtering, overall summarization performance is increased by a significant 5.9%.
There has been a good amount of research invested into improving the temporal interpretation
of text. Besides the increasing availability of annotation standards (e.g., T IME ML (Pustejovsky et
al., 2003a)) and corpora (e.g., TIDES (Ferro et
al., 2000), TimeBank (Pustejovsky et al., 2003b)),
the community has also organized three successful evaluation workshops — TempEval-1 (Verhagen et al., 2009), -2 (Verhagen et al., 2010), and
-3 (Uzzaman et al., 2013). As the state-of-theart improves, these workshops have moved away
from the piecemeal evaluation of individual temporal processing tasks and towards the evaluation
of complete end-to-end systems in TempEval-3.
We believe our understanding of the temporal information found in text is sufficiently robust, and
that there is an opportunity to now leverage this information in downstream applications. In this paper, we present our work in incorporating the use
of such temporal information into multi-document
The goal of multi-document summarization is
to generate a summary which includes the main
points from an input collection of documents with
minimal repetition of similar points. We hope to
improve the quality of the summaries that are generated by considering temporal information found
in the input text. To motivate how temporal information can be useful in summarization, let us
refer to Figure 1. The three sentences describe a
recent cyclone and a previous one which happened
in 1991. Recognizing that sentence (3) is about a
storm that had happened in the past is important
when writing a summary about the recent storm,
as it is not relevant and can likely be excluded.
It is reasonable to expect that a collection of
documents about the recent storm will contain
more references to it, compared with the earlier
one that happened in 1991. Visualized on a timeline, this will translate to more events (bolded in
Figure 1) around the time when the recent storm
occurred. There should be fewer events mentioned
in the collection for the earlier 1991 time period.
Figure 2 illustrates a possible timeline laid out
with the events found in Figure 1. The events
from the more recent storm are found together at
the same time. There are fewer events which talk
about the previous storm. Thus, temporal information does assist in identifying which sentences are
more relevant to the final summary.
Our work is significant as it addresses an important gap in the exploitation of temporal information. While there has been prior work making
use of temporal information for multi-document
(1) A fierce cyclone packing extreme winds and torrential rain smashed into Bangladesh’s southwestern coast Thursday,
wiping out homes and trees in what officials described as the worst storm in years.
(2) More than 100,000 coastal villagers have been evacuated before the cyclone made landfall.
(3) The storm matched one in 1991 that sparked a tidal wave that killed an estimated 138,000 people, Karmakar told AFP.
Figure 1: Modified extract from a news article which describes a cyclone landfall. Several events which
appear in Figure 2 are bolded.
Storm in 1991
Latest cyclone
2013-Feb-13 11:32 +0000
Figure 2: Possible timeline for events in Figure 1.
summarization, they 1) have been largely confined to helping to chronologically order content
within summaries (Barzilay et al., 1999), or 2)
focus only on the use of recency as an indicator
of saliency (Goldstein et al., 2000; Wan, 2007).
In this work we construct timelines (as a representation of temporal information) automatically
and incorporate them into a state-of-the-art multidocument summarization system. This is achieved
with 1) three novel features derived from timelines to help measure the saliency of sentences,
as well as 2) T IME MMR, a modification to the
traditional Maximal Marginal Relevance (MMR)
(Carbonell and Goldstein, 1998). TimeMMR promotes diversity by additionally considering temporal information instead of just lexical similarities. Through these, we demonstrate that temporal
information is useful for multi-document summarization. Compared to a competitive baseline, significant improvements of up to 4.1% are obtained.
Automatic temporal processing systems are not
perfect yet, and this may have an impact on their
use for downstream applications. This work additionally proposes the use of the lengths of timelines as a metric to gauge the usefulness of timelines. Together with the earlier described contributions, this metric further improves summarization,
yielding an overall 5.9% performance increase.
Related Work
Barzilay et al. (1999) were one of the first to use
time for multi-document summarization. They
recognized the importance of generating a summary which presents the time perspective of the
summarized documents correctly. They estimated
the chronological ordering of events with a small
set of heuristics, and also made use of lexical patterns to perform basic time normalization on terms
like “today” relative to the document creation
time. The induced ordering is used to present the
selected summary content, following the chronological order in the original documents.
In another line of work, Goldstein et al. (2000)
made use of the temporal ordering of documents
to be summarized. In computing the relevance of a
passage for inclusion into the final summary, they
considered the recency of the passage’s source
document. Passages from more recent documents
are deemed to be more important. Wan (2007)
and Demartini et al. (2010) made similar assumptions in their work on T IMED T EXT R ANK and entity summarization, respectively.
Instead of just considering the notion of recency, Liu et al. (2009) proposed an interesting
approach using a temporal graph. Events within
a document set correspond to vertices in their proposed graph, while edges are determined by the
temporal ordering of events. From the resulting
weakly-connected graph, the largest forests are assumed to contain key topics within the document
set and used to influence a scoring mechanism
which prefers sentences touching on these topics.
Wu (2008) also made use of the relative ordering of events. He assigned complete timestamps to events extracted from text. After laying out these events onto a timeline by making
use of these timestamps, the number of events that
happen within the same day is used to influence
sentence scoring. The motivation behind this approach is that days which have a large number of
events should be more important and more worthy
of reporting than others.
These prior works target either 1) sentence reordering, or 2) the use of recency as an indicator of
saliency. In sentence re-ordering, final summaries
are re-arranged so that the extracted sentences that
form the summary are in a chronological order.
We argue that this may not be appropriate for all
summaries. Depending on the style of writing or
journalistic guidelines, a summary can arguably be
written in a number of ways. The use of recency
as an indicator of saliency is useful, yet disregards
other accessible temporal information. If a summary of a whole sequence of events is desired, recency becomes less useful.
The work of Wu (2008) is closely related to one
of the features proposed in this paper. He had also
made use of temporal information to weight sentences to generate summaries. However his approach is guided by the number of events happening within the same time span, and relies on
event co-referencing. In this work, we have simplified this idea by dropping the need for event coreferencing (removing a source of propagated error), and augmented it with two additional features
derived from timelines. By doing so, we are able
to make better use of the available temporal information, taking into account all known events and
the time in which they occur.
A useful note here is that this work is arguably different from the Temporal Summarization (TmpSum) track at the Text Retrieval Conference (Aslam et al., 2013). Given a large stream
of data in real-time, the purpose of the TmpSum
track is to look out for a query event, and retrieve
specific details about the event over a period of
time. Systems are also expected to identify the
source sentences from which these details are retrieved. This is not the same as our approach here,
which makes use of temporal information encoded
in timelines to generate prose summaries.
To incorporate temporal information into multidocument summarization, we adopt the workflow
in Figure 3, which has two key processes: 1) temporal processing, and 2) summarization.
well-understood constructs which have often been
used to represent temporal information (Denis and
Muller, 2011; Do et al., 2012). They indicate the
temporal relationships between two basic temporal units: 1) events, and 2) time expressions (or
timexes for short). In this work, we adopt the
definitions proposed in the standardized T IME ML
annotation (Pustejovsky et al., 2003a). An event
refers to an eventuality, a situation that occurs or
an action; while a timex is a reference to a particular date or time (e.g. “2013 December 31”).
Following the “divide-and-conquer” approach
described in Verhagen et al. (2010), results from
the three temporal processing steps: 1) timex normalization, 2) event-timex temporal relationship
classification, and 3) event-event temporal relationship classification, are merged to obtain timelines (top half of Figure 3). We tap on existing
systems for each of these steps (Ng and Kan, 2012;
Strötgen and Gertz, 2013; Ng et al., 2013).
Summarization. We make use of a state-ofthe-art summarization system, SWING (Ng et al.,
2012) (bottom half of Figure 3). SWING is a supervised, extractive summarization system which
ranks sentences based on scores computed using
a set of features in the Sentence Scoring phase.
The Maximal Marginal Relevance (MMR) algorithm is then used in the Sentence Re-ordering
phase to re-order and select sentences to form the
final summary. The timelines built in the earlier temporal processing can be incorporated into
this pipeline by deriving a set of features used to
score sentences in Sentence Scoring, and as input
to the MMR algorithm when computing similarity
in Sentence Re-ordering.
Event and Timex
Temporal Processing
Summarization Pipeline
Figure 3: Incorporating temporal information into
the SWING summarization pipeline.
Temporal Processing generates timelines from
text, one for each input document. Timelines are
Timelines from Temporal Processing
A typical timeline used in this work has been
shown earlier in Figure 2. The arrowed, horizontal axis is the timeline itself. The timeline can
be viewed as a continuum of time, with points on
the timeline referring to specific moments of time.
Small solid blocks on the timeline itself are references to absolute timestamps along the timeline
(e.g., “2013-Feb-13 11:32 +0000” in the figure).
The black square boxes above the timeline denote events. Events can either occur at a specific
instance of time (e.g., an explosion), or over a period of time (e.g. a football match). Generalizing,
we refer to the time period an event takes place in
as its time span (vertical dotted lines). As a simpli-
right peak of e
biggest cluster
left peak of e
Time Span A
Time Span A+4
Figure 4: A simplified timeline illustrating how
the various timeline features can be derived.
fying assumption, events are laid out on the timeline based on the starting time of their time span.
Note that in our work, time spans may not correspond to specific instances of time, but instead
help in inferring an ordering of events. Events
which appear to the left of others take place earlier, while events within the same time span happen together over the same time period.
Sentence Scoring with Timelines
We derive three features from the constructed
timelines, which are then used for downstream
Sentence Scoring. Figure 4 shows a simplified
timeline, along with annotations that are referenced in this section to help explain how these
timeline features are derived.
1. Time Span Importance (TSI). We hypothesize that when more events happen within a particular time span, that time span is potentially more
relevant for summarization. Sentences that mention events found in such a time span should be assigned higher scores. Referring to Figure 1, whose
timeline is shown in Figure 2, we see that the time
span with the most number of events is when the
latest cyclone made landfall. Assigning higher
scores for sentences which contain events in this
time span will help us to select more relevant sentences if we want a summary about the cyclone.
Let T SL be the time span with the largest number of events in a timeline. The importance of
a time span T Si is computed by normalizing the
number of events in T Si against the number of
events in T SL . The T SI of a sentence s is then
the sum of the time span importance associated to
all the words in s:
|T Sw |
T SI(s) =
w∈s |T SL |
where T Sw denotes the time span which a word
w is associated with, and |T Sw | is the number of
events within the time span.
Contextual Time Span Importance
(CTSI). The importance of a time span may not
depend solely on the number of events that happen within it. If it is near time spans which are
“important” (i.e., one that has a large number of
events), it should also be of relative importance. A
more concrete illustration of this can also be seen
in Figure 1. Sentence (2) explains that a lot of people have been evacuated prior to the cyclone making landfall. It is imaginable that this can be useful information to be included in a summary, even
though from looking at the corresponding timeline
in Figure 2, the “evacuated” event falls in a time
span with a low importance score (i.e., the time
span only has one event). CTSI seeks to promote
sentences such as this.
We derive the CTSI of a sentence by first computing the contextual importance of words in the
sentence. We define the contextual importance of
a word found in time span T Si as a weighted sum
of the time span importance of the two nearest
peaks T Slp and T Srp found to the left and right
of T Si , respectively. In Figure 4, taking reference
from event e (shaded in black), the left peak to the
time span which e is in happens to be time span
A, while the right peak is time span A + 4. The
contribution of each peak to the weighted sum is
decayed by its distance from T Si . Formally, the
contextual time span importance of a word w can
be expressed as:
ζ(w) = α
|T Sw − T Slp |
× (1 − α)
|T Srp − T Sw |
where T Sw is the time span associated with w. Ilp
and Irp are the time span importance of the peaks
to the left and right of T Sw respectively, while
|T Sw − T Slp | and |T Srp − T Sw | are the number of time spans between the left and right peaks
of T Sw respectively. α balances the importance of
the left and right peaks, intuitively set to 0.5. The
CTSI of a sentence is computed as:
CT SI(s) = e∈Es
|Es |
where Es denotes the set of events words in s.
3. Sentence Temporal Coverage Density
(TCD). We first define the temporal coverage of a
sentence. This corresponds to the number of time
spans that the events in a sentence talk about. Suppose a sentence contains events which are associated with time spans T Sa , T Sb , T Sc . The time
spans are ordered in the sequence they appear on
the timeline. Then the temporal coverage of a sentence is defined as the number of time spans between the earliest time span T Sa and the latest
time span T Sc . Referring to Figure 4, suppose
a sentence contains the three events which have
been shaded black. The temporal coverage in this
case includes all the time spans from time span A
to time span A + 4, inclusive.
The constraint on the number of sentences that
can be included in a summary requires us to select
compact sentences which contain as many relevant facts as possible. Traditional lexical measures
may attempt to achieve this by computing the ratio of keyphrases to the number of words in a sentence (Gong and Liu, 2001). Stated equivalently,
when two sentences are of the same length, if one
contains more keyphrases, it should contain more
useful facts. TCD parallels this idea with the use
of temporal information, i.e. if two sentences are
of the same temporal coverage, then the one with
more events should carry more useful facts.
Formally, if a sentence s contains events Es =
{e1 , . . . , en }, where each event is associated with
a time span T Si , then T CD is computed using:
T CD(s) =
|Es |
|T Sn − T S1 |
where |Es | is the number of events found in s, and
|T Sn − T S1 | is the temporal coverage of s.
Enhancing MMR with TimeMMR
In the sentence re-ordering stage of the SWING
pipeline, the iterative MMR algorithm is used to
adjust the score of a candidate sentence, s. In each
iteration, s is penalized if it is lexically similar to
other sentences that have already been selected to
form the eventual summary S = {s1 , s2 , . . .}. The
motivating idea is to reduce repeated information
by preferring sentences which bring in new facts.
Incorporating temporal information can potentially improve this. In Figure 5, the sentences describe many events which took place within the
same time span. They describe the destruction
caused by a hurricane with trees uprooted and
buildings blown away. A summary about the hurricane need not contain all of these sentences as
they are all describing the same thing. However
it is not trivial for the lexically-motivated MMR
algorithm to detect that events like “passed”, “uprooted” or “damaged” are in fact repetitive.
Thus, we propose further penalizing the score
of s if it contains events that happen in similar
time spans as those contained in sentences within
S. We refer to this as T IME MMR. Modifying the
MMR equation from Ng et al. (2012):
T imeM M R(s) = Score(s) − γR2(s, S) − (1 − γ)T (s, S) (5)
where Score(s) is the score of s, S is the set of
sentences already selected to be in the summary
from previous iterations, and R2 is the predicted
ROUGE-2 score of s with respect to the already
selected sentences (S). γ is a weighting parameter
which is empirically set to 0.9 after tuning over a
development dataset. T is the proportion of events
in s which happen in the same time span as another
event in any other sentence in S. Two events are
said to be in the same time span if one happens
within the time period the other happens in. For
example, an event that takes place in “2014 June”
is said to take place within the year “2014”.
While T IME MMR is proposed here as an improvement over MMR, the premise is that incorporating temporal information can be helpful to
minimize redundancy in summaries. In future
work, one could apply it to other state-of-the-art
lexical-based approaches including that of Hendrickx et al. (2009) and Celikyilmaz and HakkaniTur (2010). We also believe the same idea can be
transplanted even to non-lexical motivated techniques such as the corpus-based similarity measure proposed by Xie and Liu (2008). We chose
to use MMR here as a proof-of-concept to demonstrate the viability of such a technique, and to easily integrate our work into SWING.
Gauging Usefulness of Timelines
Temporal processing is imperfect. Together with
the simplifying assumptions that were made in
timeline construction, our generated timelines
have errors which propagate into the summarization process. With this in mind, we selectively employ timelines to generate summaries only when
we are confident of their accuracy. This can be
done by computing a metric which can be used to
decide whether or not timelines should be used for
a particular input document collection. We refer to
this as reliability filtering.
We postulate that the length of a timeline can
serve as a simple reliability filtering metric. The
intuition for this is that for longer timelines (which
contain more events), possible errors are spread
over the entire timeline, and do not overpower any
useful signal that can be obtained from the timeline features outlined earlier. Errors are however
(1) An official in Barisal, 120 kilometres south of Dhaka, spoke of severe destruction as the 500 kilometre-wide mass of cloud
passed overhead.
(2) “Many trees have been uprooted and houses and schools blown away,” Mostofa Kamal, a district relief and rehabilitation
officer, told AFP by telephone.
(3) “Mud huts have been damaged and the roofs of several houses blown off,” said the state’s relief minister, Mortaza Hossain.
Figure 5: Extract from a news article which describes several events (bolded) happening at the same
very easily propagated into summary generation
for shorter timelines, leading to less useful results.
We incorporate this into our process as follows:
given an input document collection (which consists of 10 documents), the average size of all the
timelines for each of these 10 documents is computed. Only when this value is larger than a threshold value are the timelines used.
Experiments and Results
The proposed timeline features and T IME MMR
were implemented on top of SWING, and evaluated on the test documents from TAC-2011
(Owczarzak and Dang, 2011). SWING makes use
of three generic features and two features targeted
specifically at guided summarization. Since the
focus of this paper is on multi-document summarization, we employ only the three generic features, i.e., 1) sentence position, 2) sentence length,
and 3) interpolated n-gram document frequency
in our experiments below. Summarization evaluation is done using ROUGE-2 (R-2) (Lin and Hovy,
2003), as it has previously been shown to correlate
well with human assessment (Lin, 2004) and is often used to evaluate automatic text summarization.
The results obtained are shown in Table 1. In
the table, each row refers to a specific summarization system configuration. We also show the results of two reference systems, CLASSY (Conroy
et al., 2011) and POLYCOM (Zhang et al., 2011),
as benchmarks. CLASSY and POLYCOM are top
performing systems at TAC-2011 (ranked 2nd and
3rd by R-2 in TAC 2011, respectively; the full version of SWING was ranked 1st with a R-2 score
of 0.1380). From these results, we can see that
SWING is a very competitive baseline.
Rows 9 to 16 additionally incorporate our timeline reliability filtering. We assume that the various input document sets to be summarized are
available at the time of processing. Hence in these
experiments, the threshold for filtering is set to be
the average of all the timeline sizes over the whole
input dataset. In a production environment where
this assumption may not hold, this threshold could
Without Filtering
With Filtering
Table 1: R-2 scores after incorporating temporal
information into SWING. ‘**’ and ‘*’ denotes significant differences with respect to Row R (paired
one-tailed Student’s t-test; p < 0.05 and p < 0.1
respectively), and TMMR denotes T IME MMR.
be set by empirical tuning over a development set.
Row 1 shows the usefulness of the proposed
timeline-based features. A statistically significant
improvement of 4.1% is obtained with the use of
all three features over SWING. When we use reliability filtering (Row 9), this improvement increases to 5.9%.
The ablation test results in Rows 2 to 4 show
a drop in R-2 each time a feature is left out. With
the exception of Row 4, removing a feature lessens
the improvement in R-2 to be insignificant from
SWING’s. The same drop occurs even when reliability filtering is used (Rows 9 to 12). These indicate that all the proposed features are important
and need to be used together to be effective.
Rows 5 to 8 and Rows 13 to 16 show the effect of T IME MMR. While the results do not uniformly show that T IME MMR is effective, it can be
helpful, such as when comparing Rows 2 and 6, or
Rows 10 and 14, where R-2 improves marginally.
Looking at Rows 1 to 8, and Rows 9 to 16, we
see the importance of reliability filtering. It is able
to guide the use of timelines such that significant
improvements in R-2 over SWING are obtained.
To help visualize what the differences in these
ROUGE scores mean, Figure 7 shows two summaries1 generated for document set D1117C of the
TAC-2011 dataset. The left one is produced by the
configuration in Row 9, and the right one is produced by SWING without the use of any temporal
Feature Score
Figure 6: Breakdown of raw feature scores for sentences (L2) and (R2) from Figure 7.
The higher R-2 score obtained by the summary
on the left (0.0873) compared to the one on the
right (0.0723) suggests that temporal information
can help to identify salient sentences more accurately. A closer look at sentences (L2) and (R2)
and their R-2 scores (0.0424 and 0.0249, respectively) is instructive. Figure 6 shows the raw feature scores of both sentences. Both sentences
score similarly for the SWING features of sentence position (SP), sentence length (Length), and
interpolated n-gram document frequency (INDF);
however, the scores for all three timeline features
higher for (L2) than (R2). This helps our time sensitive system prefer (L2).
the accident itself (e.g., how much of the tower had
already been erected). In this case time span importance is able to correctly guide summary generation by favoring time spans containing events
related to the actual toppling.
Contextual Time Span Importance. CTSI
recognizes that events which happen around the
time of a big cluster of other events can be important too. The benefits of this feature can be
clearly seen in Figure 9. The summary on the left
achieved a R-2 score of 0.1215 while the one on
the right achieved 0.0861. (L2) and (L3) were
both boosted by the use of the contextual importance feature.
Figure 10 shows an extract of the timeline generated for the source document from which (L3)
is extracted. The two events inside (L3) fall in
time spans A and B marked in the figure. Their
proximity to the peak P between them gives the
sentence a higher score for CTSI. This boost results in the sentence being selected for inclusion
in the final summary. It turns out that this sentence
was lifted exactly in one of the model summaries
for this document set, resulting in a very good R-2
score when contextual importance is used.
Peak here affects time span contextual importance of A and B
We now examine the proposed 1) timeline features, 2) T IME MMR algorithm, and 3) reliability filtering metric in greater detail to gain insight
into their efficacy. For the analysis on timeline
features, we only present an analysis for TSI and
CTSI due to space constraints.
Time Span Importance. Figure 8 shows the
last sentences from a pair of summaries generated
with and without the use of TSI (all other sentences were the same). The original articles describe an accident where casualties were suffered
when a crane toppled onto a building. It is easy to
see why (L1) scores higher for R-2 — it describes
the cause of the accident just as it occurred. (R1)
however talks about events which happened before
The produced summaries are truncated to fit within a
100-word limit imposed by the TAC-2011 guidelines.
Figure 10: Extract of timeline generated for document APW ENG 20070615.0356 from the TAC2011 dataset.
Is T IME MMR Useful? The experimental results do not conclusively affirm the usefulness of
T IME MMR. However we believe it is because
the ROUGE measures that are used for evaluation are not suited for this purpose. Recall that
T IME MMR seeks to eliminate redundancy based
on time span similarities and not lexical likeness.
ROUGE, however, measures the latter.
An interesting case in point is given in Figure 11. The summary on the left is generated
using T IME MMR and achieved a lower ROUGE
score. The one on the right is generated without T IME MMR and scores higher, suggesting that
T IME MMR is not helpful. The key difference in
R-2: 0.0873
R-2: 0.0723
(L1,R1) The Army’s surgeon general criticized stories in The Washington Post disclosing problems at Walter
Reed Army Medical Center, saying the series unfairly characterized the living conditions and care for soldiers
recuperating from wounds at the hospital’s facilities.
(L2) Defense Secretary Robert Gates says people
(R2) A top Army general vowed to personally
found to have been responsible for allowing suboversee the upgrading of Walter Reed Army Medistandard living conditions for soldier outpatients at
cal Center’s Building 18, a dilapidated former hotel
Walter Reed Army Medical Center in Washington
that houses wounded soldiers as outpatients.
will be “held accountable,” although so far no one
in the Army chain of command has offered to resign.
(L3) Top Army officials visited Building 18, the
decrepit former hotel housing more than 80 recovering soldiers, outside
(R3) “I’m not sure it was an accurate representation,” Lt. Gen. Kevin Kiley, chief of the Army
Medical Command which oversees Walter Reed
and all Army health care, told reporters during a
news conference.
(R4) The Washington
Figure 7: Generated summaries for document set D1117C from the TAC-2011 dataset. Left summary is
generated by SWING+TSI+CTSI+TCD with filtering; right summary is by SWING.
R-2: 0.1683
R-2: 0.1533
(L1) A piece of steel fell and sheared off one of the
ties holding it to the building, causing it to detach
and topple, said Stephen Kaplan
(R1) About 19 of the 44 stories of the crane had
been erected and it was to be extended when a
piece of steel fell and sheared
Figure 8: Extract from summaries for document set D1137G from the TAC-2011 dataset. Left extract is
generated by SWING+TSI+CTSI+TCD; right extract is by SWING+CTSI+TCD.
the two summaries is (R3). (L3) is the equivalent
of (R4), while (L4) is the full version of the truncated (R5). T IME MMR penalizes (R3). (R3) reports that the shoe-throwing incident happened as
the U.S. President Bush appeared together with the
Iraqi Prime Minister Nouri al-Maliki. However
their joint appearance is already reported in (R1)
(and similarly (L1)). (R3) repeats what had been
presented earlier. Since (R1) and (R3) talk about
the same time span, T IME MMR down-weights
(R3). We argue that this is better even though the
ROUGE scores indicate otherwise. In future work
it will be worthwhile to consider the use of metrics
like Pyramid (Passonneau et al., 2005) which are
less bound to superficial lexicons.
Reliability Filtering. Table 2 shows the effect of varying the filtering threshold on R-2 for
the best performing configuration from Table 1
(i.e., SWING+TSI+CTSI+TCD). The result obtained in Row 9 using a threshold of 42.68 is also
re-produced for reference. T=0 means that timelines are used for all input document sets, whereas
T=100 means that no timelines are used, as the
length of the longest timeline is less than 100.
As the threshold increases from 0 to 40–50,
summarization performance improves while the
Table 2: Effect of different reliability filtering
thresholds for SWING+TSI+CTSI+TCD. ‘T’ is
the threshold used; ‘#’ is the number of input collections (out of 44) where timelines are used; ‘**’
and ‘*’ is statistical significance over SWING of
p < 0.05 and p < 0.1, respectively.
number of document sets where temporal information is used is reduced. This suggests that filtering
is successful in identifying timelines that are not
sufficiently accurate to be useful for summarization. R-2 performance peaks around a threshold
of 40. This affirms our use of the average length
of timelines as the threshold value in our earlier
experiments. Beyond 60, the R-2 scores are still
higher than that obtained by SWING, but no longer
significantly different. At these higher thresholds,
temporal information is still able to help get an improvement in R-2. However as this affects only
very few out of the 44 document sets, statistical
variances mean that these R-2 scores are no longer
R-2: 0.1215
R-2: 0.0861
((L1,R1) Caribbean coral species essential to the region’s reef ecosystems are at risk of extinction as a result of
climate change.
(L2) But destructive fishing methods and over(R2) The Coral Reef Task Force, created in the
harvesting have reduced worldwide catches by 90
Clinton administration, regularly assesses coral
percent in the past two decades.
(R3) With a finished necklace retailing for up
(L3) Scientists warn that up to half of the world’s
to 20,000 dollars (15,000 euros), red corals are
coral reefs could disappear by 2045.
among the world’s most expensive wildlife commodities.
Figure 9: Extract from summaries for document set D1131F from the TAC-2011 dataset. Left extract is
generated by SWING+TSI+CTSI+TCD; right extract is by SWING+TSI+TCD.
R-2: 0.2643
R-2: 0.2772
(L1,R1) – An Iraqi reporter threw his shoes at visiting U.S. President George W. Bush and called him a ”dog” in
Arabic during a news conference with Iraqi Prime Minister Nuri al-Maliki in Baghdad
(L2,R2) ”All I can report is it is a size 10,.
(L3) Muntadhar al-Zaidi, reporter of Baghdadiya
(R3) The incident occurred as Bush was appearing
television jumped and threw his two shoes one by
with Iraqi Prime Minister Nouri al-Maliki.
one at the president, who ducked and thus narrowly
missed being struck, raising chaos in the hall in
Baghdad’s heavily fortified green Zone.
(R4) Muntadhar al-Zaidi, reporter of Baghdadiya
(L4) The president lowered his head and the first
television jumped and threw his two shoes one by
shoe hit the American and Iraqi flags behind the
one at the president, who ducked and thus narrowly
two leaders.
missed being struck, raising chaos in the hall in
Baghdad’s heavily fortified green Zone.
(L5) The
(R5) The president lowered his head and the
Figure 11: Summaries for document set D1126E from the TAC-2011 dataset. Left summary is generated
significant from that produced by SWING.
We have shown in this work how temporal information in the form of timelines can be incorporated into multi-document summarization. We
achieve this through two means, using: 1) three
novel features derived from timelines to measure the saliency of sentences, and 2) T IME MMR
which considers time span similarity to enhance
the traditional MMR’s lexical diversity measure.
To overcome errors propagated from the underlying temporal processing systems, we proposed
a reliability filtering metric which can be used to
help decide when temporal information should be
used for summarization. The use of this metric
leads to an overall 5.9% gain in R-2 over the competitive SWING baseline.
In future work, we are keen to study our proposed timeline-related features more intrinsically
in the context of human-generated summaries.
This can help us better understand their value in
improving content selection. As noted earlier,
it will be also be useful to repeat our experiments with less lexicon-influenced measures like
the Pyramid method (Passonneau et al., 2005).
Manual assessment of the generated summaries
can also be done to give a better picture of the
quality of the summaries generated with the use
of timelines. Finally, given the importance of reliability filtering, a natural question is if there are
other metrics that can be used to get better results.
This research is supported by the Singapore National Research Foundation under its International
Research Centre @ Singapore Funding Initiative
and administered by the IDM Programme Office.
This work is also partially supported by the
National Natural Science Foundation of China
(Grant Nos. 61170189, 61370126, 61202239),
the Fund of the State Key Laboratory of Software
Development Environment (Grant No. SKLSDE2013ZX-19), and the Innovation Foundation of
Beihang University for Ph.D. Graduates (YWF13-T-YJSY-024).
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