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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
ASCII Text
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M. Saravanan, S. Raman, B. Ravindran, "A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary," Computational Intelligence and Multimedia Applications, International Conference on, pp. 167-172, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICCIMA.2005.8,
author = {M. Saravanan and S. Raman and B. Ravindran},
title = {A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary},
journal ={Computational Intelligence and Multimedia Applications, International Conference on},
volume = {0},
year = {2005},
isbn = {0-7695-2358-7},
pages = {167-172},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICCIMA.2005.8},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computational Intelligence and Multimedia Applications, International Conference on
TI - A Probabilistic Approach to Multi-document Summarization for Generating a Tiled Summary
SN - 0-7695-2358-7
SP167
EP172
A1 - M. Saravanan,
A1 - S. Raman,
A1 - B. Ravindran,
PY - 2005
KW - null
VL - 0
JA - Computational Intelligence and Multimedia Applications, International Conference on
ER -
 
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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