Títol:
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LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature
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Autor/a:
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AbuRa’ed, Ahmed; Bravo Serrano, Àlex, 1984-; Chiruzzo, Luis; Saggion, Horacio
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Abstract:
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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. |
Abstract:
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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. |
Abstract:
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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). |
Matèries:
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-Citation-based summarization -Scientific document analysis -Convolutional neural networks -Text-similarity measures |
Drets:
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Copyright © 2018 the authors
https://creativecommons.org/licenses/by-nc-sa/3.0/es/
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Tipus de document:
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Objecte de conferència Article - Versió publicada |
Publicat per:
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CEUR Workshop Proceedings
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