Please use this identifier to cite or link to this item: https://idr.l1.nitk.ac.in/jspui/handle/123456789/8032
Full metadata record
DC FieldValueLanguage
dc.contributor.authorCharitha, S.
dc.contributor.authorChittaragi, N.B.
dc.contributor.authorKoolagudi, S.G.
dc.date.accessioned2020-03-30T10:18:01Z-
dc.date.available2020-03-30T10:18:01Z-
dc.date.issued2019
dc.identifier.citation2018 IEEE Distributed Computing, VLSI, Electrical Circuits and Robotics, DISCOVER 2018 - Proceedings, 2019, Vol., , pp.7-12en_US
dc.identifier.urihttp://idr.nitk.ac.in/jspui/handle/123456789/8032-
dc.description.abstractIn this paper, we present a model for extractive multi-document text summarization using a supervised learning approach. The model uses a convolutional neural networks (CNN) which is capable of learning sentence features on its own for sentence ranking. This approach has been used in order to avoid the overhead of extracting features from sentences manually. Integer linear programming (ILP) approach has been adopted for selecting sentences to generate the summary based on sentence ranks. This ILP model minimizes the redundancy in the generated summary. We have evaluated our proposed approach on the DUC 2007 dataset and its performance is found to be competitive or better in comparison with state-of-the-art systems. � 2018 IEEE.en_US
dc.titleExtractive Document Summarization Using a Supervised Learning Approachen_US
dc.typeBook chapteren_US
Appears in Collections:2. Conference Papers

Files in This Item:
There are no files associated with this item.


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.