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               <dc:title>LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature</dc:title>
               <dc:creator>AbuRa&amp;apos;ed, Ahmed Ghassan Tawfiq</dc:creator>
               <dc:creator>Bravo Serrano, Àlex, 1984-</dc:creator>
               <dc:creator>Chiruzzo, Luis</dc:creator>
               <dc:creator>Saggion, Horacio</dc:creator>
               <dc:subject>Citation-based summarization</dc:subject>
               <dc:subject>Scientific document analysis</dc:subject>
               <dc:subject>Convolutional neural networks</dc:subject>
               <dc:subject>Text-similarity measures</dc:subject>
               <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>
               <dc:description>In this paper we present several systems developed to partic-&#xd;
ipate in the 3rd Computational Linguistics Scienti c Document Summa-&#xd;
rization Shared challenge which addresses the problem of summarizing&#xd;
a scienti c paper taking advantage of its citation network (i.e., the pa-&#xd;
pers that cite the given paper). Given a cluster of scienti c documents&#xd;
where one is a reference paper (RP) and the remaining documents are&#xd;
papers citing the reference, two tasks are proposed: (i) to identify which&#xd;
sentences in the reference paper are being cited and why they are cited,&#xd;
and (ii) to produce a citation-based summary of the reference paper using&#xd;
the information in the cluster. Our systems are based on both supervised&#xd;
(Convolutional Neural Networks) and unsupervised techiques taking ad-&#xd;
vantage of word embeddings representations and features computed from&#xd;
the linguistic and semantic analysis of the documents.</dc:description>
               <dc:description>This work is (partly) supported by the Spanish Ministry of Economy and Com-&#xd;
petitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-&#xd;
2015-0502) and by the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER,&#xd;
UE).</dc:description>
               <dc:date>2018-07-19T07:43:12Z</dc:date>
               <dc:date>2018-07-19T07:43:12Z</dc:date>
               <dc:date>2018</dc:date>
               <dc:type>info:eu-repo/semantics/conferenceObject</dc:type>
               <dc:type>info:eu-repo/semantics/publishedVersion</dc:type>
               <dc:relation>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>
               <dc:relation>info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R</dc:relation>
               <dc:rights>Copyright © 2018 the authors</dc:rights>
               <dc:rights>https://creativecommons.org/licenses/by-nc-sa/3.0/es/</dc:rights>
               <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
               <dc:publisher>CEUR Workshop Proceedings</dc:publisher>
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