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COLING
2010

Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization

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Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization
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 clusterbased 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.
Xiaoyan Cai, Wenjie Li, Ouyang You, Hong Yan
Added 13 May 2011
Updated 13 May 2011
Type Journal
Year 2010
Where COLING
Authors Xiaoyan Cai, Wenjie Li, Ouyang You, Hong Yan
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