A similarity-based abstract argumentation approach to extractive text summarization

Stefano Ferilli*, Andrea Pazienza, Sergio Angelastro, Alessandro Suglia

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

Sentence-based extractive summarization aims at automatically generating shorter versions of texts by extracting from them the minimal set of sentences that are necessary and sufficient to cover their content. Providing effective solutions to this task would allow the users to save time in selecting the most appropriate documents to read for satisfying their information needs or for supporting their decision-making tasks. This paper proposes 2 contributions: (i) it defines a novel approach, based on abstract argumentation, to select the sentences in a text that are to be included in the summary; (ii) it proposes a new strategy for similarity assessment among sentences, adopting a different similarity measure than those traditionally exploited in the literature. The effectiveness of the proposed approach was confirmed by experimental results obtained on the English subset of the benchmark MultiLing2015 dataset.

Original languageEnglish
Title of host publicationAI*IA 2017 Advances in Artificial Intelligence. AI*IA 2017
EditorsFloriana Esposito, Stefano Ferilli, Francesca A. Lisi, Roberto Basili
PublisherSpringer
Pages87-100
Number of pages14
ISBN (Electronic)9783319701691
ISBN (Print)9783319701684
DOIs
Publication statusPublished - 7 Nov 2017
Event16th International Conference on Italian Association for Artificial Intelligence 2017 - Bari, Italy
Duration: 14 Nov 201717 Nov 2017

Publication series

NameLecture Notes in Computer Science
Volume10640
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Italian Association for Artificial Intelligence 2017
Abbreviated titleAI*IA 2017
Country/TerritoryItaly
CityBari
Period14/11/1717/11/17

Keywords

  • Abstract argumentation
  • Information extraction
  • Text summarization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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