DSpace logo

Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2024-11-15T09:05:50Z-
dc.date.available2024-11-15T09:05:50Z-
dc.date.issued2020-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050920306566-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391-
dc.description.abstractWith introduction of deep learning techniques their has been an increase in intelligent classification of text in many applications. Advances in automatic text summarization using deep learning technique is prime focus of research now a days. Earlier traditional approaches for extractive text summarization have been heavily dependent on human engineered features. However, it is a laborious and tedious task. In this paper, a data-driven approach has been used to generate extractive summaries using deep learning. Approach proposed uses paraphrasing techniques to classify sentences as a candidate sentence for inclusion in summary or not.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectComputer Scienceen_US
dc.subjectExtractive Text Summarizationen_US
dc.subjectParaphrase Detectionen_US
dc.subjectNatural Language Processing (NLP)en_US
dc.subjectDeep Learning (DL)en_US
dc.titleDeep Extractive Text Summarizationen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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.