Abura'ed A, Bravo A, Chiruzzo L, Saggion H. LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using Regression and Convolutions for Cross-document Semantic Linking and Summarization of Scholarly Literature. Proceedings of the 3rd Joint Workshop on Bibliometric-enhanced Information Retrieval and Natural Language Processing for Digital Libraries (BIRNDL 2018)

In this paper we present several systems developed to participate in the 3rd Computational Linguistics Scientific Document Summarization Shared challenge which addresses the problem of summarizing a scientific paper taking advantage of its citation network (i.e., the papers that cite the given paper). Given a cluster of scientific 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 advantage of word embeddings representations and features computed from the linguistic and semantic analysis of the documents.