№1, 2010
MULTIDOCUMENT SUMMARIZATION THROUGH CLUSTERING AND RANKING OF SENTENCES
Two staged unsupervised approach to multidocument summarization is offered. At the first stage a set of documents are grouped into topics and at the second stage the informative sentences are extracted. The topics are defined through sentences clustering, and the informative sentences are extracted using the ranking algorithm. It is shown that summarization results depend on the clustering method, ranking algorithm and similarity measure. Experiments on open benchmarks DUC2001 and DUC2002 have shown that the offered clustering methods and ranking algorithm outperform the k-means method and the ranking algorithms PageRank and HITS. (p. 26-37)
Keywords: multidocument summarization, sentence clustering, sentence ranking.
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- http://duc.nist.gov
