Topic Models for Comparatie Summarization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F13%3A43919441" target="_blank" >RIV/49777513:23520/13:43919441 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Topic Models for Comparatie Summarization
Original language description
This paper aims to sum up our work in the area of comparative summarization and to present our results. The focus of comparative summarization is the analysis of input documents and the creation of summaries which depict the most significant differencesin them. We experiment with two well known methods ? Latent Semantic Analysis and Latent Dirichlet Allocation ? to obtain the latent topics of documents. These topics can be compared and thus we can learn the main factual differences and select the mostsignificant sentences into the output summaries. Our algorithms are briefly explained in section 2 and their evaluation on the TAC 2011 dataset with the ROUGE toolkit is then presented in section 3.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2013
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
TSD 2013
ISBN
978-3-642-40584-6
ISSN
0302-9743
e-ISSN
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Number of pages
7
Pages from-to
568-574
Publisher name
Springer
Place of publication
Heidelberg
Event location
Plzeň
Event date
Sep 1, 2013
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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