Event-based summarization using time features

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

Event-based summarization using time features

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

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

3 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 language	English
Title of host publication	Computational Linguistics and Intelligent Text Processing - 8th International Conference, CICLing 2007, Proceedings
Pages	563-574
Number of pages	12
Publication status	Published - 20 Dec 2007
Event	8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007 - Mexico City, Mexico Duration: 18 Feb 2007 → 24 Feb 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4394 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007
Country/Territory	Mexico
City	Mexico City
Period	18/02/07 → 24/02/07

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Cite this

Wu, M., Li, W., Lu, Q., & Wong, K. F. (2007). Event-based summarization using time features. In Computational Linguistics and Intelligent Text Processing - 8th International Conference, CICLing 2007, Proceedings (pp. 563-574). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4394 LNCS).

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title = "Event-based summarization using time features",

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.",

author = "Mingli Wu and Wenjie Li and Qin Lu and Wong, {Kam Fai}",

year = "2007",

month = dec,

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Wu, M, Li, W, Lu, Q & Wong, KF 2007, Event-based summarization using time features. in Computational Linguistics and Intelligent Text Processing - 8th International Conference, CICLing 2007, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4394 LNCS, pp. 563-574, 8th Annual Conference on Intelligent Text Processing and Computational Linguistics, CICLing 2007, Mexico City, Mexico, 18/02/07.

Event-based summarization using time features. / Wu, Mingli; Li, Wenjie; Lu, Qin et al.
Computational Linguistics and Intelligent Text Processing - 8th International Conference, CICLing 2007, Proceedings. 2007. p. 563-574 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4394 LNCS).

Research output: Chapter in book / Conference proceeding › Conference article published in proceeding or book › Academic research › peer-review

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AB - 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.

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M3 - Conference article published in proceeding or book

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Event-based summarization using time features

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ASJC Scopus subject areas

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