Artículos regulares

 

Multi-document Summarization using Tensor Decomposition

 

Marina Litvak and Natalia Vanetik

 

Shamoon College of Engineering, Beer Sheva, Israel. marinal@sce.ac.il, natalyav@sce.ac.il.

 

Article received on 31/12/2013.
Accepted on 12/02/2014.

 

Abstract

The problem of extractive text summarization for a collection of documents is defined as selecting a small subset of sentences so the contents and meaning of the original document set are preserved in the best possible way. In this paper we present a new model for the problem of extractive summarization, where we strive to obtain a summary that preserves the information coverage as much as possible, when compared to the original document set. We construct a new tensor-based representation that describes the given document set in terms of its topics. We then rank topics via Tensor Decomposition, and compile a summary from the sentences of the highest ranked topics.

Keywords: Tensor decomposition, multilingual multi-focument summarization.

 

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Acknowledgments

Authors are grateful to Igor Vinokur for the plugin implementation and technical support.

 

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