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A Fuzzy Approach for Sentences Relevance Assessment in Multi-document Summarization

  • Eduardo Valladares-Valdés
  • Alfredo Simón-CuevasEmail author
  • José A. Olivas
  • Francisco P. Romero
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 950)

Abstract

Text summarization is becoming an indispensable solution for dealing with the exponential growth of textual and unstructured information in digital format. In this paper, an unsupervised method for extractive multi-document summarization is presented. This method combines the use of a semantic graph for representing textual contents and identify the most relevant topics with the processing of several sentences features applying a fuzzy logic perspective. A fuzzy aggregation operator is applied in the sentences relevance assessment process as a contribution to the multi-document summarization process. The method was evaluated with the Spanish and English texts collection of MultiLing 2015. The obtained results were measured through ROUGE metrics and compared with those obtained by other solutions reported from MultiLing2015.

Keywords

Multi-document summarization Extractive summarization Semantic graph Sentence feature Fuzzy aggregation operator 

Notes

Acknowledgments

This work has been partially supported by FEDER and the State Research Agency (AEI) of the Spanish Ministry of Economy and Competition under grant MERINET: TIN2016-76843-C4-2-R (AEI/FEDER, UE).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Universidad Tecnológica de La Habana José Antonio Echeverría, CujaeLa HabanaCuba
  2. 2.Universidad de Castilla-La ManchaCiudad RealSpain

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