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Computational Intelligence and Multimedia Applications, International Conference on (2005)
Las Vegas, Nevada
Aug. 16, 2005 to Aug. 18, 2005
ISBN: 0-7695-2358-7
pp: 167-172
M. Saravanan , Indian Institute of Technology at Madras
B. Ravindran , Indian Institute of Technology at Madras
S. Raman , Indian Institute of Technology at Madras
ABSTRACT
Due to data overload and time-critical nature of information need, automatic summarization of documents plays a significant role in information retrieval and text data mining. This paper discusses the design of a multi-document summarizer that uses Katz?s K-mixture model for term distribution. The model helps in ranking the sentences by a modified term weight assignment. The system has been evaluated against the frequently occurring sentences in the summaries generated by a set of human subjects. Our system outperforms other autosummarizers at different extraction levels of summarization with respect to the ideal summary, and is close to the ideal summary at 40% extraction level.
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CITATION
M. Saravanan, B. Ravindran, S. Raman, "A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary", Computational Intelligence and Multimedia Applications, International Conference on, vol. 00, no. , pp. 167-172, 2005, doi:10.1109/ICCIMA.2005.8
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