Co-clustering sentences and terms for multi-document summarization

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. © 2011 Springer-Verlag.