LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature

Mostra el registre complet Registre parcial de l'ítem

• dc.contributor.author AbuRa'ed, Ahmed Ghassan Tawfiqca
• dc.contributor.author Bravo Serrano, Àlex, 1984-ca
• dc.contributor.author Chiruzzo, Luisca
• dc.contributor.author Saggion, Horacioca
• dc.date.accessioned 2018-07-19T07:43:12Z
• dc.date.available 2018-07-19T07:43:12Z
• dc.date.issued 2018
• dc.description Comunicació presentada al congrés BIRNDL 2018, 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries que va tenir lloc el 21 de juliol de 2018 a Ann Arbor, Estats Units.
• dc.description.abstract In this paper we present several systems developed to partic- ipate in the 3rd Computational Linguistics Scienti c Document Summa- rization Shared challenge which addresses the problem of summarizing a scienti c paper taking advantage of its citation network (i.e., the pa- pers that cite the given paper). Given a cluster of scienti c documents where one is a reference paper (RP) and the remaining documents are papers citing the reference, two tasks are proposed: (i) to identify which sentences in the reference paper are being cited and why they are cited, and (ii) to produce a citation-based summary of the reference paper using the information in the cluster. Our systems are based on both supervised (Convolutional Neural Networks) and unsupervised techiques taking ad- vantage of word embeddings representations and features computed from the linguistic and semantic analysis of the documents.
• dc.description.sponsorship This work is (partly) supported by the Spanish Ministry of Economy and Com- petitiveness under the Maria de Maeztu Units of Excellence Programme (MDM- 2015-0502) and by the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE).
• dc.format.mimetype application/pdf
• dc.identifier.citation Abura'ed A, Bravo À, Chiruzzo L, Saggion H. LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature. In: Mayr P, Chandrasekaran MK, Jaidka K, editors. BIRNDL 2018. 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries; 2018 Jul 21; Ann Arbor, MI. [place unknown]: CEUR; 2018. p. 150-63.
• dc.identifier.uri http://hdl.handle.net/10230/35201
• dc.language.iso eng
• dc.publisher CEUR Workshop Proceedingsca
• dc.relation.ispartof Mayr P, Chandrasekaran MK, Jaidka K, editors. BIRNDL 2018. 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries; 2018 Jul 21; Ann Arbor, MI. [place unknown]: CEUR; 2018. p. 150-63.
• dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
• dc.rights Copyright © 2018 the authors
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri https://creativecommons.org/licenses/by-nc-sa/3.0/es/
• dc.subject.keyword Citation-based summarization
• dc.subject.keyword Scientific document analysis
• dc.subject.keyword Convolutional neural networks
• dc.subject.keyword Text-similarity measures
• dc.title LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literatureca
• dc.type info:eu-repo/semantics/conferenceObject
• dc.type.version info:eu-repo/semantics/publishedVersion