Saggion, HoracioAbuRa'ed, Ahmed Ghassan TawfiqRonzano, Francesco2016-06-282016-06-282016Saggion H, AbuRa'ed A, Ronzano F. Trainable citation-enhanced summarization of scientific articles. In: Cabanac G, Chandrasekaran MK, Frommholz I, Jaidka K, Kan M, Mayr P, Wolfram D, editors. Proceedings of the Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL); 2016 June 23; Newark, United States. [place unknown]: CEUR Workshop Proceedings; 2016. p. 175-86.1613-0073http://hdl.handle.net/10230/26970In 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.application/pdfengCopyright © 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.info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/openAccess