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Integrating Document Clustering and Multidocument Summarization

ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 2011年第3期5卷 14-14页

作者： Wang, Dingding Zhu, Shenghuo Li, Tao Chi, Yun Gong, Yihong Florida Int Univ Sch Comp Sci Miami FL 33199 USA NEC Labs Amer Inc Cupertino CA 95014 USA

Document understanding techniques such as document clustering and multidocument summarization have been receiving much attention recently. Current document clustering methods usually represent the given collection of documents as a document-term matrix and then conduct the clustering process. Although many of these clustering methods can group the documents effectively, it is still hard for people to capture the meaning of the documents since there is no satisfactory interpretation for each document cluster. A straightforward solution is to first cluster the documents and then summarize each document cluster using summarization methods. However, most of the current summarization methods are solely based on the sentence-term matrix and ignore the context dependence of the sentences. As a result, the generated summaries lack guidance from the document clusters. In this article, we propose a new language model to simultaneously cluster and summarize documents by making use of both the document-term and sentence-term matrices. By utilizing the mutual influence of document clustering and summarization, our method makes;(1) a better document clustering method with more meaningful interpretation;and (2) an effective document summarization method with guidance from document clustering. Experimental results on various document datasets show the effectiveness of our proposed method and the high interpretability of the generated summaries.

关键词： Document clustering multidocument summarization nonnegative matrix factorization with given bases

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