LaSTUS/TALN+INCO @ CL-SciSumm 2018 - Using regression and convolutions for cross-document semantic linking and summarization of scholarly literature

Abstract

In this paper we present several systems developed to partic- ipate in the 3rd Computational Linguistics Scienti c Document Summa- rization Shared challenge which addresses the problem of summarizing a scienti c paper taking advantage of its citation network (i.e., the pa- pers that cite the given paper). Given a cluster of scienti c 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 ad- vantage of word embeddings representations and features computed from the linguistic and semantic analysis of the documents.