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Obtaining single document summaries using latent dirichlet allocation

Nagesh, Karthik and Murty, Narasimha M (2012) Obtaining single document summaries using latent dirichlet allocation. In: 19th International Conference, ICONIP 2012, November 12-15, 2012, Doha, Qatar.

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Official URL: http://dx.doi.org/10.1007/978-3-642-34478-7_9


In this paper, we present a novel approach that makes use of topic models based on Latent Dirichlet allocation(LDA) for generating single document summaries. Our approach is distinguished from other LDA based approaches in that we identify the summary topics which best describe a given document and only extract sentences from those paragraphs within the document which are highly correlated given the summary topics. This ensures that our summaries always highlight the crux of the document without paying any attention to the grammar and the structure of the documents. Finally, we evaluate our summaries on the DUC 2002 Single document summarization data corpus using ROUGE measures. Our summaries had higher ROUGE values and better semantic similarity with the documents than the DUC summaries.

Item Type: Conference Proceedings
Additional Information: Copyright of this article belongs to IEEE.
Keywords: Single Document Summaries; Latent Dirichlet Allocation; SVM; Naïve Bayes Classifier
Department/Centre: Division of Electrical Sciences > Computer Science & Automation
Depositing User: Id for Latest eprints
Date Deposited: 21 May 2013 11:44
Last Modified: 21 May 2013 11:44
URI: http://eprints.iisc.ac.in/id/eprint/46555

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