Combined Cluster Based Ranking for Web Document Using  Semantic Similarity

Journal Title: IOSR Journals (IOSR Journal of Computer Engineering) - Year 2014, Vol 16, Issue 1

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

Authors and Affiliations

V. Anthoni sahaya balan

Keywords

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  • EP ID EP115625
  • DOI -
  • Views 112
  • Downloads 0

How To Cite

V. Anthoni sahaya balan (2014).  Combined Cluster Based Ranking for Web Document Using  Semantic Similarity. IOSR Journals (IOSR Journal of Computer Engineering), 16(1), 6-11. https://europub.co.uk/articles/-A-115625