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

ACL ID C10-1016
Title Simultaneous Ranking and Clustering of Sentences: A Reinforcement Approach to Multi-Document Summarization
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2010
Authors

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 per...