Co-Feedback Ranking for query-focused summarization

Furu Wei, Wenjie Li, Yanxiang He

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

4 Citations (Scopus)

Abstract

In this paper, we propose a novel ranking framework - Co-Feedback Ranking (Co-FRank), which allows two base rankers to supervise each other during the ranking process by providing their own ranking results as feedback to the other parties so as to boost the ranking performance. The mutual ranking refinement process continues until the two base rankers cannot learn from each other any more. The overall performance is improved by the enhancement of the base rankers through the mutual learning mechanism. We apply this framework to the sentence ranking problem in query-focused summarization and evaluate its effectiveness on the DUC 2005 data set. The results are promising.
Original languageEnglish
Title of host publicationACL-IJCNLP 2009 - Joint Conf. of the 47th Annual Meeting of the Association for Computational Linguistics and 4th Int. Joint Conf. on Natural Language Processing of the AFNLP, Proceedings of the Conf.
Pages117-120
Number of pages4
Publication statusPublished - 1 Dec 2009
EventJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009 - Suntec, Singapore
Duration: 2 Aug 20097 Aug 2009

Conference

ConferenceJoint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and 4th International Joint Conference on Natural Language Processing of the AFNLP, ACL-IJCNLP 2009
Country/TerritorySingapore
CitySuntec
Period2/08/097/08/09

ASJC Scopus subject areas

  • Language and Linguistics
  • Linguistics and Language

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