Using Regression and Convolutions for Cross-document Semantic Linking and Summarization of Scholarly Literature
Ahmed Ghassan Tawfiq AbuRa'ed (ahmed (point) aburaed <at> upf (point) edu)
Universitat Pompeu Fabra
Wednesday 12 September 2018 at 11:30 AM
Salle 3195, Pavillon André-Aisenstadt
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.
To receive weekly talk announcements, please send an e-mail to email@example.com. Simply write a message containing the single line 'subscribe ralli' (without the quotes, with a double 'l' in 'ralli').
See all the weekly talks for the year:1991 1992 1993 1994 1995 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022