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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Full text [356 kB]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 Ježek
Phone: +420 377632475
E-mail: jezek_ka@kiv.zcu.cz
WWW: https://cs.wikipedia.org/wiki/Karel_Je%C5%BEek_(informatik)
Karel is the former group coordinator and a supervisor of PhD students working at research projects of this Group.
Related Projects:
Automatic Text Summarisation | |
| Authors: | Josef Steinberger, Karel Ježek, Michal Campr, Jiří Hynek |
| Desc.: | Automatic text summarisation using various text mining methods, mainly Latent Semantic Analysis (LSA). |