Simultaneous ranking and clustering of sentences: A reinforcement approach to multi-document summarization

Xiaoyan Cai, Wenjie Li, You Ouyang, Hong Yan

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

21 Citations (Scopus)


Multi-document summarization aims to produce a concise summary that contains salient information from a set of source documents. In this field, sentence ranking has hitherto been the issue of most concern. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering was recently explored in the literature in order to provide more informative summaries. Existing cluster based ranking approaches applied clustering and ranking in isolation. As a result, the ranking performance will be inevitably influenced by the clustering result. In this paper, we propose a reinforcement approach that tightly integrates ranking and clustering by mutually and simultaneously updating each other so that the performance of both can be improved. Experimental results on the DUC datasets demonstrate its effectiveness and robustness.
Original languageEnglish
Title of host publicationColing 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference
Number of pages9
Publication statusPublished - 1 Dec 2010
Event23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China
Duration: 23 Aug 201027 Aug 2010


Conference23rd International Conference on Computational Linguistics, Coling 2010

ASJC Scopus subject areas

  • Language and Linguistics
  • Computational Theory and Mathematics
  • Linguistics and Language

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