An Introduction to the Summarization of Evolving
Events: Linear and Non-linear Evolution
Stergos D. Afantenos
, Konstantina Liontou
, Maria Salapata
and Vangelis
Software and Knowledge Engineering Laboratory
Institute of Informatics and Telecommunications,
National Center for Scientific Research (NCSR) “Demokritos”
Institute of Language and Speech Processing
Abstract. This paper examines the summarization of events that evolve through
time. It discusses different types of evolution taking into account the time in
which the incidents of an event are happening and the different sources reporting
on the specific event. It proposes an approach for multi-document summarization
which employs “messages” for representing the incidents of an event and cross-
document relations that hold between messages according to certain conditions.
The paper also outlines the current version of the summarization system we are
implementing to realize this approach.
1 Introduction
The exchange of information is of outmost importance for humans. Through the his-
tory of humankind it has taken many forms, from gossiping to the publication of news
through dedicated media. More recently, the Internet has given a new perspective to
this human faculty, making the exchange of information much more easy and virtually
Naturally this has caused some problems. Imagine, for example, that someone wants
to keep track of an event that is being described on various news sources, over the
Internet, as it evolves through time. The problem is that there exist a plethora of news
sources making very difficult for someone to compare the different versions of the story
in each source. Automatic text summarization is a solution to this information overflow
problem. In this paper we propose a general framework for the automatic summarization
of evolving events, i.e. the summarization of events that evolve through time.
A crucial question, that can possibly arise at this point, concerns the definition of
the “event”. In the Topic Detection and Tracking (TDT) research an event is described
as “something that happens at some specific time and place” ([1], p 3; see also [2]).
The inherent notion of time is what distinguishes the event from the more general term
topic. For example, incidents which include hostages are regarded as topics, while a
particular incident, such as the one concerning the two Italian women that were kept
as hostages by an Iraqi group in 2004, is regarded as an event. In our discussion about
“events” we will adopt this definition provided by the TDT research.
D. Afantenos S., Liontou K., Salapata M. and Karkaletsis V. (2005).
An Introduction to the Summarization of Evolving Events: Linear and Non-linear Evolution.
In Proceedings of the 2nd International Workshop on Natural Language Understanding and Cognitive Science, pages 91-99
DOI: 10.5220/0002564200910099
In the Multi-document Summarization community, a consensus that has emerged is
that in order to summarize a set of related documents, one has to identify similarities
and differences among the documents ([3,4]; see also [5] and [6]). Yet, no consensus
has been reached concerning as to where those similarities and differences should be
targeted. In our work we propose that the similarities and differences, at least for evolv-
ing events, should be viewed under two perspectives: time and source, through cross-
document relations. We call synchronic relations those relations that are concerned with
the similarities and differences, between the various sources, on the same temporal hori-
zon and diachronic relations those relations that are more concerned with the evolution
of an event as it is being described by one source.
Summarization of evolving events should not be confused with evolving summaries.
Evolving summaries were originally proposed, but not implemented, by [7] (p.149)
as follows: An evolving summary S
is the summary of a story, numbered A
when the stories numbered A
to A
have already been processed and presented in a
summarized form to the user. Summary S
differs from its predecessor, S
, because
it contains new information and omits information from S
”. What we propose, instead,
is a framework which will enable the creation of summaries of evolving events.
Section 2 discusses the different kinds of evolution in terms of the time the incidents
of an event are happening and in terms of the rate with which the various news sources
are emitting their reports. Section 3 introduces the notion of messages which we use
for representing the various incidents of an event. Section 4 discusses the two types of
cross-document relations (synchronic and diachronic) which hold between messages.
Section 5 outlines the system developed so far that realizes our approach, as well as
other options we are currently investigating.
2 Kinds of Evolution
This work studies the summarization of events that evolve through time, as they are
being described by various sources. In this study we came to the conclusion that we
should distinguish between the evolution of an event in time and the rate of reporting
about an evolving event from various sources.
Concerning the evolution of an event we distinguish between two types of evolution:
linear and non-linear evolution. In linear evolution the major incidents of an event are
happening in constant and possibly predictable quanta of time. This means that if the
first incident q
happens at time t
, then each subsequent incident q
will come at time
= t
+ n t, where t is the constant amount of time with which the incidents are
happening. In non-linear evolution, in contrast, we cannot distinguish any meaningful
pattern in the order that the major incidents of an event are happening. This distinction
is depicted in Figure 1 in which the evolution of two different events is depicted with
the dark solid circles.
Linearly evolving events have a fair proportion in the world. They are related with
human activities which occur at regular intervals. One such example can be the descrip-
tions of various athletic events which occur regularly. In particular we have examined
the descriptions of football matches [8]. On the other hand, one can argue that most
of the events that we find in the news stories are non-linearly evolving events. They
can vary from political ones, such as elections or various international political issues,
to airplane crashes or terroristic events. Currently we are investigating the domain of
incidents which involve hostages.
u uuu u u uu uu u u uu
e e e e e e e e e e e
u u u u u u u u u u u
e e e e ee e e e e e e
e e e e e
Linear Evolution
Non-linear Evolution
Synchronous Emition
Asynchronous Emition
Fig.1. Linear and Non-linear evolution
In terms of the reporting on an event from various sources we can distinguish be-
tween synchronous and asynchronous emission of reports. This distinction is depicted
in Figure 1 with the white circles. In most of the cases, when we have an event that
evolves linearly we will also have a synchronous emission of reports, since the various
sources can easily adjust to the pattern of the evolution of an event. This cannot be said
for the case of non-linear evolution, resulting thus in asynchronous emission of reports
by the various sources.
In Figure 2 we represent two events which evolve linearly and non-linearly and for
which the sources report synchronously and asynchronously respectively. The horizon-
tal axis in this figure represents the number of reports per source on a particular event.
The vertical axis represents the time, in minutes, that the documents are published. The
first event concerns descriptions of football matches. In this particular event we have
constant reports weekly, i.e. every 10800 minutes, from 3 different sources. The lines
for each source fall on top of each other since they publish simultaneously. The second
event concerns a terroristic group in Iraq which kept as hostages two Italian women
threatening to kill them, unless their demands were fulfilled. In the figure we depict 5
sources. The number of reports that each source is making varies from ve to twelve,
in a period of time of about 23 days. As we can see from the figure, most of the sources
begin reporting almost instantaneously, except one which delays its report for about
twelve days. Another source, although it reports almost immediately, it delays consid-
erably later reports.
The linearity or non-linearity of an evolving event, as well as the rate of sources
emission, affects our summarization approach which is based on the exploitation of
the similarities and differences that exist synchronically and diachronically between
the documents. The cross-document relations, and the way that they are affected by
linearity, will be explained in more detail in section 4. In the following section we will
concentrate on the notion of messages for representing the incidents of an event.
0 1 2 3 4 5 6 7 8 9 10 11
Number of Reports
Time in minutes
0 1 2 3 4 5 6 7 8 9 10 11 12
Number of Reports
Time in minutes
Fig.2. Linear and Non-linear evolution
3 Messages
Each event is composed from various simpler incidents. For example, in the football
domain, such incidents can be the performance of a player or a team, the goals that are
achieved, the possible injuries of players, etc. In a domain with hostages such incidents
can be the occupation of a building, the negotiations, the demands of the terrorists, the
fact that they freed a hostage, etc.
We use messages to represent those incidents. Each message is composed of two
parts: its type and a list of arguments which take their values from an ontology for the
specific domain:
type ( arg
, . . . , arg
where arg
Domain Ontology
The message type represents the type of the incident, whilst the arguments represent
the main entities that are involved in this incident. It is possible that some messages
may be accompanied by some constraints on their arguments, which reflect various
pragmatic constraints. These messages are similar structures (although simpler ones)
with the templates used in the Message Understanding Conferences (MUC).
Each message is also linked to a specific source and time. In other words, if we
have a message m, then we have associated with it two extra pieces of information,
m.time and m.source. Concerning the source, it is inherited by the document
that contains the message. This cannot be said for the timeas well, since the time of the
incidents might be different from the emission time. This is expressed in the document
by a temporal expression. Thus, in order to determine the time of a message we should
interpret this expression in relation to the time of the publication of the document.
See [8].
7 toc.html
Linear Non-linear
performance (entity, in what, time span, value) negotiate (entity
, entity
, about)
entity : Player or Team
what : Action Area
time span : Minute or Duration
value : Degree
: Person
: Person
about : Activity
Examples of messages’ specifications, for a linear and a non-linear domain are
shown in the above table. The arguments for each message come from the domain ontol-
ogy. Thus, for example, the Activity argument in the second message corresponds
to a set of activities which are defined in the ontology of the domain. The specifications
for the first message come from the domain of football matches [8] and it represents the
performance of a player or a team for a specific time-span and a specific action area (e.g.
in the defense). The specifications of the second message come from the topic which is
related with hostages, which we currently investigate. This message represents the fact
that we have a negotiation between two entities concerning a specific activity (e.g. the
release of some hostages).
4 Cross-document Relations
Cross-document relations hold between messages and are distinguished into synchronic
and diachronic.
Synchronic relations try to identify the similarities and differences that two sources
have, at about the same time. In the case of linear or synchronous evolution all the
sources report in the same time. Thus in most of the cases the incidents described
in each document refer to the time that the article was published. Yet, in some cases
we might have temporal expressions in the text that modify the time that a message
might refer. In such cases, before establishing a synchronic relation, we should place
this message in the appropriate time horizon. In the case of non-linear asynchronous
evolution this phenomenon is predominant. Each source reports at irregular intervals,
possibly mentioning incidents that happened long before the publication of the article,
and which another source might have already mentioned in an article published ear-
lier (see the second part of Figure 2). In this case we shouldn’t rely any more to the
publication of an article, but instead on the time tag that the messages have, which has
been appropriately modified according to the temporal expressions found in the text.
Once this has been performed, we should then establish a time window in which we
should consider the messages, and thus the relations, as synchronic. This time window,
depending on the domain, can vary from some hours to some days.
Diachronic relations, on the other hand, try to capture the similarities and differ-
ences, through time, that exist for an event as it is being described by the same source.
In this sense, diachronic relations do not exhibit the problems of time that the syn-
chronic relations do.
Cross-document relations, in our viewpoint, are domain dependent, since they rep-
resent pragmatic information which depends on the domain.
Examples of synchronic
relations can be agreement, disagreement, elaboration, generalization, etc. Examples
of diachronic relations can be positive or negative graduation, stability, continuation,
repetition, etc.
In more formal terms, if we represent a relation r as a pair of messages hm
, m
where m
and m
are two messages, then a relation will be synchronic iff
.time = m
.time and m
.source 6= m
and diachronic iff
.time > m
.time and m
.source = m
We have to note that a relation has a directionality. As is evident, diachronically a re-
lation can hold from a past time to a future time. In the case of a synchronic relation
(e.g. agreement) a relation can have both directions, in which case we have in fact two
In order to define a relation in a domain we have to provide a name for it, and
describe the conditions under which it will hold. The name of the relation is in fact
pragmatic information, which we will be able to exploit during the generation of the
summary. The conditions under which a relation between two messages holds are rep-
resented in terms of values of their arguments, as well as their corresponding time and
Suppose, for example, that we have two identical messages. If they have the same
temporal tag, but belong to different sources, then we have an
relation. If,
on the other hand, they have the same source but chronological distance one or higher,
then we can speak, for example, of a
relation. Thus we see that, apart from the
characteristics that the arguments of a message pair hm
, m
i should exhibit, the source
and temporal distance also play a role for that pair to be characterized as a relation.
In Figure 3 we can see the difference, in terms of synchronic relations, between a
domain which evolves linearly and has a synchronous emission of reports and a do-
main which evolves non-linearly and has asynchronous emission of reports. In the first
case we have two identical performance messages (see the table of page 5), from
two documents which have been published at the same time. Thus, and according to
the specifications of the synchronic relations [9], we have an
relation. In
the second case we have two identical negotiate messages from documents that
have different publication times. Yet, in the text that defines those messages, we have
a temporal expression which modifies the time tag for one of the messages, making
them refer on the same day. Thus, again we have an
relation, although the
documents which contain the messages have not been published on the same day.
In the same figure you can see two diachronic relations. In the linearly evolving case
we have two performance messages
This does not mean that we do not believe that domain independent relations could not possibly
exist. An example could be the relations agreement and disagreement, which can obviously be
independent of domain.
Source 1 Source 2
Linear/Synchrnous Non−linear/Asynchronous
Source 1 Source 2
Fig.3. Examples of synchronic and diachronic relations
performance (entity
, in
, time span
, value
performance (entity
, in
, time span
, value
which have identical arguments, except that value
< value
. In this case, and
according to the specifications for the relations of the domain [8] we have a
diachronic relation. In the second case we have two different messages
start (entity
, activity
end (entity
, activity
where entity
= entity
and activity
= activity
. In this case, ac-
cording to the specifications, we have a
diachronic relation. Note that in
the first case we have a diachronic relation that holds between the same message types,
while in the second case the diachronic relation holds between different message types.
Also, in the first case the documents that contain the messages have distance one, i.e. the
one follows immediately the other, while in the second case they have greater distance.
There may be also cases where an event is being described by one source but not
from the others. Since we need at least two messages from different sources in order
to have a synchronic relation, we will not connect that message with another one, thus
possibly missing an important piece of information that a source is reporting. An
relation could be introduced to handle such cases.
5 Potential Computational Approaches
An initial study of a linearly evolving domain is presented in [8]. In [9] we present a
system which automatically extracts the messages and the relations from the text. The
messages extraction sub-system involves two processing stages, one for the identifica-
tion of the messages’ types and one for the filling in of its arguments. During the first
stage a classifier is trained. The word lemmas and the Named Entities are used in the
training vectors. The argument filling is performed using heuristics. The sub-system
implementing the extraction of relations exploits the conditions under which a relation
holds, as described in the specifications of each relation.
Currently we are investigating a topic which evolves non-linearly with asynchro-
nous emission of reports, namely that of incidents involving hostages. For this topic,
apart from performing the above experiments concerning the extraction of the mes-
sages and the relations, we are also implementing an algorithm which identifies the
various temporal expressions in the text. This is essential since, as we have noted in
sections 3 and 4 in order to identify the synchronic relation in a non-linearly evolv-
ing domain with asynchronous emission of reports, we should not rely anymore on the
time an article was published. Instead we should recognize the time that a message is
referring to, according to the temporal expressions which characterize this message.
Additionally, we plan to enhance our classification experiments, as well as the fill-
ing in of the messages’ arguments, exploiting syntactic processing and incorporating
6 Concluding Remarks
This work has discussed the summarization of evolving events in terms of their evo-
lution in time linear, non-linear and the source synchronous, asynchronous.
Of course, we are not the first to introduce the notion of time in summarization. [10]
work on temporal summarization is such a case. In their work they take the results
from a TDT system for an event, and they put all the sentences one after the other in
chronological order, regardless of the document that it belonged to, creating a stream
of sentences. Then they apply two statistical measures, usefulness and novelty, to each
ordered sentence. The aim is to extract those sentences which have a score over a cer-
tain threshold. This approach differs from ours in various ways. Firstly, they do not
distinguish between the sources, while we try to incorporate in our system the differ-
ent viewpoints that the various sources might have, and present them to the user. Also,
they are not concerned with the evolution of the events; instead they try to detect novel
information. Finally, we have an abstractive system, while they have an extractive one.
In terms of the source dimension, as far as we know, this has not been discussed
Another point that should be stressed concerns the use of the cross-document rela-
tions. In the past there have been several attempts to incorporate relations, in one form
or another, for the creation of a summary. [11], for example, proposed the Cross-do-
cument Structure Theory (CST) which incorporated a set of 24 domain-independent
relations that exist between various textual units across documents. In a later paper [12]
reduce that set to 17 relations and perform experiments with human judges. Those ex-
periments revealed several interesting results. For example, human judges annotated
only sentences, ignoring the other textual units (phrases, paragraphs, documents) that
the theory suggests. Additionally, there was a rather small inter-judge agreement con-
cerning the type of relation that connects two sentences. Nevertheless, [13] and [14]
continue this work using Machine Learning algorithms to identify the cross-document
relations. We have to note here that although some cross-document relations such as
agreement and disagreement might be independent of the domain, we believe that in
general cross-document relations do depend on the domain. Another difference with
our work is that our relations concentrate on identifying the similarities and differences
between the sources, in two different axes: synchronically and diachronically. In other
words, we try to capture through those relations the points of difference between the
sources, as well as the evolution of an event.
We are currently studying the summarization of non-linear events and extend our
summarization system in order to improvethe performance of the extraction sub-system.
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