A light-weight summarizer based on language model with relative entropy

Authors:
Chandan Kumar;Prasad Pingali;Vasudeva Varma
Affiliations:
International Institute of Information Technology, Hyderabad, India;International Institute of Information Technology, Hyderabad, India;International Institute of Information Technology, Hyderabad, India
Venue:
Proceedings of the 2009 ACM symposium on Applied Computing
Year:
2009

Citing 4
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Abstract

A new method for sentence extraction on the basis of language model with relative entropy is presented in this paper. The proposed technique first builds a sentence language model and document cluster language model respectively for the sentence and the documents. The sentences are then ranked according to the relative entropies of the estimated document language model with respect to the estimated sentence language model. The overall results on DUC and MSE corpus demonstrate that the proposed approach outperforms some of the best reported results for generic multi-document summarization.