A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary

Authors:
M. Saravanan;S. Raman;B. Ravindran
Affiliations:
Indian Institute of Technology at Madras;Indian Institute of Technology at Madras;Indian Institute of Technology at Madras
Venue:
ICCIMA '05 Proceedings of the Sixth International Conference on Computational Intelligence and Multimedia Applications
Year:
2005

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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驴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.