Event-based summarization using time features

Mingli Wu, Wenjie Li, Qin Lu, Kam Fai Wong

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

2 Citations (Scopus)

Abstract

We investigate whether time features help to improve event-based summarization. In mis paper, events are defined as event terms and the associated event elements. While event terms represent the actions themselves, event elements denote action arguments. After anchoring events on the time line, two different statistical measures are employed to identify importance of events on each day. Experiments show that the combination of tfidf weighting scheme and time features can improve the quality of summaries significantly. The improvement can be attributed to its capability to represent the trend of news topics depending on event temporal distributions.
Original languageEnglish
Title of host publicationComputational Linguistics and Intelligent Text Processing - 8th International Conference, CICLing 2007, Proceedings
Pages563-574
Number of pages12
Publication statusPublished - 20 Dec 2007
Event8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007 - Mexico City, Mexico
Duration: 18 Feb 200724 Feb 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4394 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007
Country/TerritoryMexico
CityMexico City
Period18/02/0724/02/07

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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