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Multi-document summarization using sentence-based topic models

  • Florida International University
  • NEC Corporation

科研成果: 书/报告/会议事项章节会议稿件同行评审

166 引用 (Scopus)

摘要

Most of the existing multi-document summarization methods decompose the documents into sentences and work directly in the sentence space using a term-sentence matrix. However, the knowledge on the document side, i.e. the topics embedded in the documents, can help the context understanding and guide the sentence selection in the summarization procedure. In this paper, we propose a new Bayesian sentence-based topic model for summarization by making use of both the term-document and term-sentence associations. An efficient variational Bayesian algorithm is derived for model parameter estimation. Experimental results on benchmark data sets show the effectiveness of the proposed model for the multi-document summarization task.

源语言英语
主期刊名ACL-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.
297-300
页数4
出版状态已出版 - 2009
已对外发布
活动Joint 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, 新加坡
期限: 2 8月 20097 8月 2009

丛书

姓名ACL-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.

会议

会议Joint 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
国家/地区新加坡
Suntec
时期2/08/097/08/09

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