Title:
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Trainable citation-enhanced summarization of scientific articles
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Author:
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Saggion, Horacio; AbuRa’ed, Ahmed; Ronzano, Francesco
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Abstract:
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In order to cope with the growing number of relevant scientific publications to consider at a given time, automatic text summarization is a useful technique. However, summarizing scientific papers poses important challenges for the natural language processing community. In recent years a number of evaluation challenges have been proposed to address the problem of summarizing a scientific paper taking advantage of its citation network (i.e., the papers that cite the given paper). Here, we present our trainable technology to address a number of challenges in the context of the 2nd Computational Linguistics Scientific Document/nSummarization Shared Task. |
Abstract:
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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), the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE) and the European Project Dr. Inventor (FP7-ICT-2013.8.1 - Grant no: 611383). |
Rights:
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Copyright © 2016 for the individual papers by the papers' authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.
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Document type:
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Conference Object Article - Published version |
Published by:
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CEUR Workshop Proceedings
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