Multi-document Summarization using Tensor Decomposition

Authors

  • Marina Litvak Shamoon College of Engineering
  • Natalia Vanetik Shamoon College of Engineering

DOI:

https://doi.org/10.13053/cys-18-3-2026

Keywords:

Tensor Decomposition, Multilingual Multi-Document Summarization

Abstract

The problem of extractive text summarizationfor a collection of documents is defined as selecting asmall subset of sentences so the contents and meaningof the original document set are preserved in the bestpossible way. In this paper we present a new modelfor the problem of extractive summarization, where westrive to obtain a summary that preserves the informationcoverage as much as possible, when compared to theoriginal document set. We construct a new tensor-basedrepresentation that describes the given document setin terms of its topics. We then rank topics viaTensor Decomposition, and compile a summary from thesentences of the highest ranked topics.

Author Biographies

Marina Litvak, Shamoon College of Engineering

obtained a Ph.D. in Information SystemsEngineering from Ben-Gurion University ofthe Negev in 2010. She is currently a facultymember at Department of Software Engineering ofShamoon College of Engineering in Beer Sheva,Israel. Her research interests include informationretrieval, text mining, automated summarization,social networks analysis, and recommender systems.

Natalia Vanetik, Shamoon College of Engineering

obtained a Ph.D. in ComputerScience from Ben-Gurion University of the Negevin 2009. She is currently a faculty member atDepartment of Software Engineering of ShamoonAcademic College of Engineering in Beer Sheva,Israel. Her research interests include data mining,combinatorial optimization, text mining and textanalysis and biological data mining.

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Published

2014-09-29

Issue

Vol. 18 No. 3 (2014): 18(3) 2014 Thematic issue: Computational linguistics (Guest editor: Alexander Gelbukh)

Section

Articles

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