NDLI: Mixture of topic model for multi-document summarization

Content Provider	IEEE Xplore Digital Library
Author	Liu Na Li Ming-xia Lu Ying Tang Xiao-jun Wang Hai-wen Xiao Peng
Copyright Year	2014
Description	Author affiliation: Sch. of Inf. Sci. & Eng., Dalian Polytech. Univ., Dalian, China (Liu Na; Li Ming-xia; Lu Ying; Tang Xiao-jun; Wang Hai-wen; Xiao Peng)
Abstract	Based on LDA(Latent Dirichlet Allocation) topic model, a generative model for multi-document summarization, namely Titled-LDA that simultaneously models the content of documents and the titles of document is proposed. This generative model represents each document with a mixture of topics, and extends these approaches to title modeling by allowing the mixture weights for topics to be determined by the titles of the document. In the mixing stage, the algorithm can learn the weight in an adaptive asymmetric learning way based on two kinds of information entropies. In this way, the final model incorporated the title information and the content information appropriately, which helped the performance of summarization. The experiments showed that the proposed algorithm achieved better performance compared the other state-of-the-art algorithms on DUC2002 corpus.
Starting Page	5168
Ending Page	5172
File Size	179611
Page Count	5
File Format	PDF
ISBN	9781479937073
e-ISBN	9781479937080
DOI	10.1109/CCDC.2014.6853102
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-31
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models multi-document summarization Computational modeling topic model LDA Computational linguistics Resource management Mathematical model Data mining Information entropy
Content Type	Text
Resource Type	Article

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