Chinese multi-document summarization using adaptive clustering and global search strategy

Authors:
Dexi Liu;Yanxiang He;Donghong Ji;Hua Yang;Zhao Wu
Affiliations:
School of Physics, Xiangfan University, Xiangfan, P.R. China and School of Computer, Wuhan University, Wuhan, P.R. China;School of Computer, Wuhan University, Wuhan, P.R. China;Institute for Infocomm Research, Singapore;School of Computer, Wuhan University, Wuhan, P.R. China;School of Physics, Xiangfan University, Xiangfan, P.R. China
Venue:
PRICAI'06 Proceedings of the 9th Pacific Rim international conference on Artificial intelligence
Year:
2006

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Abstract

Multi-document summarization has become a key technology in natural language processing. This paper proposes a strategy for Chinese multidocument summarization based on clustering and sentence extraction. As for clustering, we propose two heuristics to automatically detect the proper number of clusters: the first one makes full use of the summary length fixed by the user; the second is a stability method, which has been applied to other unsupervised learning problems. We also discuss a global searching method for sentence selection from the clusters. To evaluate our summarization strategy, an extrinsic evaluation method based on classification task is adopted. Experimental results on news document set show that the new strategy can significantly enhance the performance of Chinese multi-document summarization.