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00925njm 22002777a 4500 dc AbuRa'ed, Ahmed Ghassan Tawfiq author Bravo Serrano, Àlex, 1984- author Chiruzzo, Luis author Saggion, Horacio author 2018-07-19T07:43:12Z 2018-07-19T07:43:12Z 2018 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. 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. 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). Citation-based summarization Scientific document analysis Convolutional neural networks Text-similarity measures LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature