An empirical assessment of citation information in scientific summarization

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To access the full text documents, please follow this link: http://hdl.handle.net/10230/28267

dc.contributor.author	Saggion, Horacio
dc.contributor.author	Ronzano, Francesco
dc.date	2016
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.citation	http://dx.doi.org/10.1007/978-3-319-41754-7_30
dc.identifier.uri	http://hdl.handle.net/10230/28267
dc.format	application/pdf
dc.language.iso	eng
dc.publisher	Springer
dc.relation	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	info:eu-repo/grantAgreement/EC/FP7/611383
dc.rights	info:eu-repo/semantics/openAccess
dc.rights	© Springer The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-319-41754-7_30.
dc.subject	Citation-based summarization
dc.subject	Scientific text mining
dc.subject	Summary evaluation
dc.title	An empirical assessment of citation information in scientific summarization
dc.type	info:eu-repo/semantics/conferenceObject
dc.type	info:eu-repo/semantics/acceptedVersion
dc.description.abstract	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.abstract	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).

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