Comunicació presentada a: CL-SciSumm 2017, organitzat com a part de The 2nd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2017) i conjuntament amb The 40th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2017)

In recent years there has been an increasing interest in approaches to scienti c summarization that take advantage of the citations a research paper has received in order to extract its main contributions. In this context, the CL-SciSumm 2017 Shared Task has been proposed to address citation-based information extraction and summarization. In this paper we present several systems to address three of the CL-SciSumm tasks. Notably, unsupervised systems to match citing and cited sentences (Task 1A), a supervised approach to identify the type of information being cited (Task 1B), and a supervised citation-based summarizer (Task 2).

This work is supported by the Spanish Ministry of Economy and Competitiveness 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).