Combined Cluster Based Ranking for Web Document Using Semantic Similarity

Abstract

Multidocument summarization is a set of documents on the same topic, the output is a paragraph length summary. Since documents often cover a number of topic themes with each theme represented by a cluster of highly related sentences, sentence clustering has been explored in the literature in order to provide more informative summaries. An existing cluster-based summarization approach that directly generates clusters first and with ranking next. Ranking distribution of sentences in each cluster should be quite different from each other, which may serve as features of cluster, we propose an integrated approach that overcomes the drawback that we provide ranking for same meaning of different words. As a clustering result to improve or refine the sentence ranking results. The effectiveness of the proposed approach is demonstrated by both the cluster quality analysis and the summarization evaluation conducted on our simulated datasets.