Multiple News Articles Summarization based on Event Reference Information
Masaharu YOSHIOKA Makoto HARAGUCHI Hokkaido University{yoshioka,makoto}@db-ei.eng.hokudai.ac.jp

BackgroundEventz Multiple News Articles Summarizationz Text: Multiple news articles about particular eventsz Characteristics:Not a single document summarization
zRedundant description
zImportant events might be referred several times indifferent articles

z Information that describes facts and related information on particular date.
z Root is a word that dominates an event (verb that represents action
or noun that represents subject or object)z Modifier is words that modify root word. Words are categorized into
several groups, such as subject and object words for verbs andadjective and adnominal words for nouns.
z Negative represents modality of expression.
z Depth is a path length between Root of the event and root of thesentence in dependency analysis tree.
z Date is a date that characterize the event. This slot is not a required
slot to define an event.
z ArticleDate is a date that the article was published.
z Chunks represents list of word positions in a sentence.

Objectivez Proposal of a method for Multiple News Articles Summarization
based on Event Reference Information

Extraction of Events from a Sentencez We apply Cabocha to obtain dependency analysis tree.
z We select verbs and nouns that have modification words ascandidates of “Root'' for events.
z We extract “Modifier” information from dependency analysis tree. At
this time, we classify types of modifier by using POS tag andpostpositional particle.
z When we can extract date information from the sentence, we set thisdate as “Date'' for events that has dependency with date words.
z “ArticleDate'' is obtained from article information.
z “Depth'' and “Chunks'' are calculated by comparing event information
with the dependency analysis tree.

input

Root墜落Depth 2
Subject 飛行機
ArticleDate 990909
Chunks 1,0

Root事故Depth 1
Subject
ArticleDate 990909
Chunks 2,1
動詞 墜落,する

Root発生
Depth 0Subject 事故
ArticleDate 990909
Chunks 3,2

飛行機が墜落する事故が発生した。

Cabocha
飛行機が-D墜落する-D

Event Reference

事故が-D

z Similar Eventsz Compare “Date” and “ArticleDate”
z Compare “Root” and Corresponding element of “Root” and“Modifiers”

発生した。

An Example of Events Extraction from a Sentence
Important Sentence Extraction by using
PageRank Algorithmz PageRank : Calculate importance of pages by using link structurerrr∞ = lim rirr
ri +1 = M × rii →∞z Our algorithmz Node: a sentencez Link: a bidirectional link exists when two nodes shares same
events or wordsz Transition matrix is calculated by a combination of event
reference information and word reference information
zMe: transition probability based on event reference
biased with event importance.
zMw: transition probability based on word reference
biased with IDF.

Text Reordering and Compaction by using Event
Reference Informationz Keep sentence order in an article and chronological order of articles
z When sentences comes from different articles in a same date, findsimilar sentence in its original article and set order based on it.
z Remove redundant event description from a sentencez An event that has similar events in the extracted event set isselected as a candidate one to remove.
z Keep words, which is a “Root” element of an event and also
belong to other non-redundant events.
2. 飛行機が墜落した事故の原因が判明した。
Root 墜落Depth 3
Subject 飛行機
ArticleDate 990910
Chunks 1,0

mij = β × meij + (1 − β ) × mwijz Topic-Sensitive PageRank (Calculate importance of pages biased
with initial importance vector)z Initial importance vector: Sentence position in an article

rrr
ri +1 = (1 − α ) * M × ri + α * v

Article 1
1.
Event A Event B
2.
Event B Event C
3.
Event A Event D

Event A

RootRootEvent B

事故
Depth 2
SubjectArticleDate 990910
Chunks 2,1
動詞 墜落,するinput1. 飛行機が墜落する事故が発生した。
2. 飛行機が墜落した事故の原因が判明した。

Redundant events

2. 飛行機が墜落した事故の原因が判明した。

判明
Depth 0Subject 事故,原因
ArticleDate 990910
Chunks 4,3,2

Removal of words
by using redundant
event information

1. 飛行機が墜落する事故が発生した。
2. 事故の原因が判明した。

Article 2
1.
Event A Event E
2.
Event E Event F
3.
Event A Event G

Event E

Link Structure based on Event Reference Information
Evaluation Results of Importance Sentence Extraction
ShortLong

Event
only

Coverage

0.325

0.313

Precision

0.491

0.540

Event&
Word

Coverage

0.323

0.341

Precision

0.523

0.592

Wordonly

Coverage

0.313

0.344

Precision

0.521

0.593

An Example of Text Compaction
Evaluated Resultz Importance sentence extractionz Usage of event reference information only is not good comparedwith usage of word information.
z Abstractionz Positive
zq00: Our method for removing redundant description
works well.
zq08,q02: Our sentence reordering methods works well.
zq04: Our system tends to select most frequent description
because of word reference information.
z Negative
zq01: Removal of redundant information is too naïve. We
assume that usage of anaphoric word is necessary to solve
this problem.
zq10: Our method of removing words from a sentence is
too naive