Extractive summarization based on event term temporal relation graph and critical chain

Maofu Liu, Wenjie Li, Huijun Hu

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

In this paper, we investigate whether temporal relations among event terms can help improve event-based extractive summarization and text cohesion of machine-generated summaries. Using the verb semantic relation, namely happens-before provided by VerbOcean, we construct an event term temporal relation graph for source documents. We assume that the maximal weakly connected component on this graph represents the main topic of source documents. The event terms in the temporal critical chain identified from the maximal weakly connected component are then used to calculate the significance of the sentences in source documents. The most significant sentences are included in final summaries. Experiments conducted on the DUC 2001 corpus show that extractive summarization based on event term temporal relation graph and critical chain is able to organize final summaries in a more coherent way and accordingly achieves encouraging improvement over the well-known tf*idf-based and PageRank-based approaches.
Original languageEnglish
Title of host publicationInformation Retrieval Technology - 5th Asia Information Retrieval Symposium, AIRS 2009, Proceedings
Pages87-99
Number of pages13
DOIs
Publication statusPublished - 1 Dec 2009
Event5th Asia Information Retrieval Symposium, AIRS 2009 - Sapporo, Japan
Duration: 21 Oct 200923 Oct 2009

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5839 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference5th Asia Information Retrieval Symposium, AIRS 2009
CountryJapan
CitySapporo
Period21/10/0923/10/09

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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