An empirical assessment of citation information in scientific summarization

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  • dc.contributor.author Saggion, Horacioca
  • dc.contributor.author Ronzano, Francescoca
  • dc.date.accessioned 2017-03-21T08:46:29Z
  • dc.date.issued 2016
  • dc.description Comunicació presentada a la NLDB 2016, 21st International Conference on Applications of Natural Language to Information Systems, celebrada a Salford (Regne Unit) del 22 al 24 de juny del 2016.
  • dc.description.abstract Considering the recent substantial growth of the publication rate of scientific results, nowadays the availability of effective and automated techniques to summarize scientific articles is of utmost importance. In this paper we investigate if and how we can exploit the citations of an article in order to better identify its relevant excerpts. By relying on the BioSumm2014 dataset, we evaluate the variation in performance of extractive summarization approaches when we consider the citations to extend or select the contents of an article to summarize. We compute the maximum ROUGE-2 scores that can be obtained when we summarize a paper by considering its contents together with its citations. We show that the inclusion of citation-related information brings to the generation of better summaries.
  • dc.description.sponsorship This work is (partly) supported by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502) and by the European Project Dr. Inventor (FP7-ICT-2013.8.1 - Grant no: 611383).
  • dc.format.mimetype application/pdfca
  • dc.identifier.citation Ronzano F, Saggion H. An empirical assessment of citation information in scientific summarization. In: Métais E, Meziane F, Saraee M, Sugumaran V, Vadera S, editors. Natural language processing and information systems. 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016; 2016 June 22-24; Salford (UK). Switzerland: Springer; 2016. p. 318-25. DOI: 10.1007/978-3-319-41754-7_30
  • dc.identifier.doi http://dx.doi.org/10.1007/978-3-319-41754-7_30
  • dc.identifier.uri http://hdl.handle.net/10230/28267
  • dc.language.iso eng
  • dc.publisher Springerca
  • dc.relation.ispartof Métais E, Meziane F, Saraee M, Sugumaran V, Vadera S, editors. Natural language processing and information systems. 21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016; 2016 June 22-24; Salford (UK). Switzerland: Springer; 2016. p. 318-25.
  • dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/611383
  • dc.rights © Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-41754-7_30.
  • dc.rights.accessRights info:eu-repo/semantics/openAccess
  • dc.subject.keyword Citation-based summarization
  • dc.subject.keyword Scientific text mining
  • dc.subject.keyword Summary evaluation
  • dc.title An empirical assessment of citation information in scientific summarizationca
  • dc.type info:eu-repo/semantics/conferenceObject
  • dc.type.version info:eu-repo/semantics/acceptedVersion

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