Text Summarization Model based on Facility Location Problem

e propose a novel multi-document generic summarization model based on the budgeted median problem, which is a facility location problem. The summarization method based on our model is an extractive method, which selects sentences from the given document cluster and generates a summary. Each sentence...

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Bibliographic Details
Published inTransactions of the Japanese Society for Artificial Intelligence Vol. 25; no. 1; pp. 174 - 182
Main Authors Takamura, Hiroya, Okumura, Manabu
Format Journal Article
LanguageEnglish
Published Tokyo The Japanese Society for Artificial Intelligence 2010
Japan Science and Technology Agency
Subjects
Artificial intelligence
Asymmetry
budgeted median problem
Clusters
Expert systems
facility location problem
integer linear programming
Mathematical models
Position (location)
Sentences
Summaries
text summarization
Texts
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Summary:e propose a novel multi-document generic summarization model based on the budgeted median problem, which is a facility location problem. The summarization method based on our model is an extractive method, which selects sentences from the given document cluster and generates a summary. Each sentence in the document cluster will be assigned to one of the selected sentences, where the former sentece is supposed to be represented by the latter. Our method selects sentences to generate a summary that yields a good sentence assignment and hence covers the whole content of the document cluster. An advantage of this method is that it can incorporate asymmetric relations between sentences such as textual entailment. Through experiments, we showed that the proposed method yields good summaries on the dataset of DUC'04.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:1346-0714
1346-8030
DOI:10.1527/tjsai.25.174