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 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.
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