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2008
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Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization

8 years 12 months ago
Multi-document summarization via sentence-level semantic analysis and symmetric matrix factorization
Multi-document summarization aims to create a compressed summary while retaining the main characteristics of the original set of documents. Many approaches use statistics and machine learning techniques to extract sentences from documents. In this paper, we propose a new multi-document summarization framework based on sentence-level semantic analysis and symmetric non-negative matrix factorization. We first calculate sentence-sentence similarities using semantic analysis and construct the similarity matrix. Then symmetric matrix factorization, which has been shown to be equivalent to normalized spectral clustering, is used to group sentences into clusters. Finally, the most informative sentences are selected from each group to form the summary. Experimental results on DUC2005 and DUC2006 data sets demonstrate the improvement of our proposed framework over the implemented existing summarization systems. A further study on the factors that benefit the high performance is also conducted....
Dingding Wang, Tao Li, Shenghuo Zhu, Chris H. Q. D
Added 15 Dec 2010
Updated 15 Dec 2010
Type Journal
Year 2008
Where SIGIR
Authors Dingding Wang, Tao Li, Shenghuo Zhu, Chris H. Q. Ding
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