Query-sensitive mutual reinforcement chain and its application in query-oriented multi-document summarization

Furu Wei, Wenjie Li, Qin Lu, Yanxiang He

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

73 Citations (Scopus)

Abstract

Sentence ranking is the issue of most concern in document summarization. Early researchers have presented the mutual reinforcement principle (MR) between sentence and term for simultaneous key phrase and salient sentence extraction in generic single-document summarization. In this work, we extend the MR to the mutual reinforcement chain (MRC) of three different text granularities, i.e., document, sentence and terms. The aim is to provide a general reinforcement framework and a formal mathematical modeling for the MRC. Going one step further, we incorporate the query influence into the MRC to cope with the need for query-oriented multi-document summarization. While the previous summarization approaches often calculate the similarity regardless of the query, we develop a query-sensitive similarity to measure the affinity between the pair of texts. When evaluated on the DUC 2005 dataset, the experimental results suggest that the proposed query-sensitive MRC (Qs-MRC) is a promising approach for summarization.
Original languageEnglish
Title of host publicationACM SIGIR 2008 - 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Proceedings
Pages283-290
Number of pages8
DOIs
Publication statusPublished - 15 Dec 2008
Event31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008 - Singapore, Singapore
Duration: 20 Jul 200824 Jul 2008

Conference

Conference31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008
CountrySingapore
CitySingapore
Period20/07/0824/07/08

Keywords

  • Mutual reinforcement chain
  • Query-oriented summarization
  • Query-sensitive similarity
  • Ranking algorithms

ASJC Scopus subject areas

  • Information Systems
  • Software

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