A context-sensitive manifold ranking approach to query-focused multi-document summarization

Xiaoyan Cai, Wenjie Li

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

7 Citations (Scopus)

Abstract

Query-focused multi-document summarization aims to create a compressed summary biased to a given query. This paper presents a context-sensitive approach based on manifold ranking of sentences to this summarization task. The proposed context enhanced manifold ranking approach not only looks at the sentence itself, but also considers its surrounding contextual information. Compared to the existing manifold ranking approach which totally ignores the contextual information of a sentence, this approach can capture more additional relevant information which is especially necessary for formulating the relationships between short text snippets like sentences. Experiments are conducted on the DUC 2005 and DUC 2006 data sets and the ROUGE evaluation results demonstrate the advantages of the proposed approach.
Original languageEnglish
Title of host publicationPRICAI 2010
Subtitle of host publicationTrends in Artificial Intelligence - 11th Pacific Rim International Conference on Artificial Intelligence, Proceedings
Pages27-38
Number of pages12
DOIs
Publication statusPublished - 3 Nov 2010
Event11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010 - Daegu, Korea, Republic of
Duration: 30 Aug 20102 Sep 2010

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6230 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference11th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2010
Country/TerritoryKorea, Republic of
CityDaegu
Period30/08/102/09/10

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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