Abstract
Two issues are crucial to multi-document summarization: diversity and redundancy. Content within some topically-related articles are usually redundant while the topic is delivered from diverse perspectives. This paper presents a co-clustering based multi-document summarization method that makes full use of the diverse and redundant content. A multi-document summary is generated in three steps. First, the sentence-term co-occurrence matrix is designed to reflect diversity and redundancy. Second, the co-clustering algorithm is performed on the matrix to find globally optimal clusters for sentences and terms in an iterative manner. Third, a more accurate summary is generated by selecting representative sentences from the optimal clusters. Experiments on DUC2004 dataset show that the co-clustering based multi-document summarization method is promising.
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Xia, Y., Zhang, Y., Yao, J. (2011). Co-clustering Sentences and Terms for Multi-document Summarization. In: Gelbukh, A. (eds) Computational Linguistics and Intelligent Text Processing. CICLing 2011. Lecture Notes in Computer Science, vol 6609. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19437-5_28
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DOI: https://doi.org/10.1007/978-3-642-19437-5_28
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