Comparative Summarization via Latent Dirichlet Allocation

Comparative Summarization via Latent Dirichlet Allocation

This paper aims to explore the possibility of using Latent Dirichlet Allocation (LDA) for multi-document comparative summarization which detects the main differences in documents. The first two sections of this paper focus on the definition of comparative summarization and a brief explanation of using the LDA topic model in this context. In the last three sections, our novel method for multi-document com- parative summarization using LDA is presented and also its results are compared with the results of a similar method based on Latent Semantic Analysis.

Keywords: comparative summarization, latent dirichlet allocation, latent semantic analysis, topic model

Year: 2013

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Authors of this publication:


Michal Campr


E-mail: mcampr@kiv.zcu.cz
WWW: http://home.zcu.cz/~mcampr/

Michal graduated from the University of West Bohemia in 2011, specialized in software engineering. He is interested in text summarization.

Karel Je┼żek


Phone:  +420 377632475, 377632400
E-mail: jezek_ka@kiv.zcu.cz
WWW: http://www-kiv.zcu.cz/~jezek_ka/

Karel is a group coordinator and a supervisor of PhD students working at research projects of this Group.

Related Projects:


Project

Automatic Text Summarisation

Authors:  Josef Steinberger, Karel Je┼żek, Michal Campr, Ji┼Ö├ş Hynek
Desc.:Automatic text summarisation using various text mining methods, mainly Latent Semantic Analysis (LSA).