In this paper we propose a clustering-based hybrid approach for multi-document summarization which integrates sentence clustering, local recommendation and global search. For sentence clustering, we adopt a stability-based method which can determine the optimal cluster number automatically. We weight sentences with terms they contain for local sentence recommendation of each cluster. For global selection, we propose a global criterion to evaluate overall performance of a summary. Thus the sentences in the final summary are determined by not only the configuration of individual clusters but also the overall performance. This approach successfully gets top-level performance running on corpus of DUC04. © Springer-Verlag Berlin Heidelberg 2006.
CITATION STYLE
Yu, N., Donghong, J., Lingpeng, Y., Zhengyu, N., & Tingling, H. (2006). Multi-document summarization using a clustering-based hybrid strategy. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4182 LNCS, pp. 608–614). Springer Verlag. https://doi.org/10.1007/11880592_53
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