Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/22427
Title: A spectral analysis approach to document summarization : clustering and ranking sentences simultaneously
Authors: Cai, X
Li, W 
Keywords: Document summarization
Sentence clustering
Sentence ranking
Spectral analysis
Issue Date: 2011
Publisher: Elsevier
Source: Information sciences, 2011, v. 181, no. 18, p. 3816-3827 How to cite?
Journal: Information sciences 
Abstract: Automatic document summarization aims to create a compressed summary that preserves the main content of the original documents. It is a well-recognized fact that a document set often covers a number of topic themes with each theme represented by a cluster of highly related sentences. More important, topic themes are not equally important. The sentences in an important theme cluster are generally deemed more salient than the sentences in a trivial theme cluster. Existing clustering-based summarization approaches integrate clustering and ranking in sequence, which unavoidably ignore the interaction between them. In this paper, we propose a novel approach developed based on the spectral analysis to simultaneously clustering and ranking of sentences. Experimental results on the DUC generic summarization datasets demonstrate the improvement of the proposed approach over the other existing clustering-based approaches.
URI: http://hdl.handle.net/10397/22427
ISSN: 0020-0255
EISSN: 1872-6291
DOI: 10.1016/j.ins.2011.04.052
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