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Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05)
A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary
Las Vegas, Nevada
August 16-August 18
ISBN: 0-7695-2358-7
M. Saravanan, Indian Institute of Technology at Madras
S. Raman, Indian Institute of Technology at Madras
B. Ravindran, Indian Institute of Technology at Madras
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.
Citation:
M. Saravanan, S. Raman, B. Ravindran, "A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary," iccima, pp.167-172, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005
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