DTIC MdM Strategic Program: Artificial and Natural Intelligence for ICT and beyond

Abura’ed A, Chiruzzo L, Saggion H, Accuosto P, Bravo A. LaSTUS/TALN @ CLSciSumm-17: Cross-document Sentence Matching and Scientific Text Summarization Systems. Proceedings of the Second Joint Workshop on Bibliometric Enhanced Information Retrieval and Natural Language Processing for Digital Libraries

In recent years there has been an increasing interest in approaches to scientific 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). 

Additional material:

• Poster
• Publication availale in open access at the web of the conference
• Postprint at UPF e-repository