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 Field | Value | Language |
---|---|---|
dc.contributor.author | Sharma, Yashvardhan | - |
dc.date.accessioned | 2024-11-15T09:05:50Z | - |
dc.date.available | 2024-11-15T09:05:50Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1877050920306566 | - |
dc.identifier.uri | http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391 | - |
dc.description.abstract | With 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.iso | en | en_US |
dc.publisher | Elsevier | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Extractive Text Summarization | en_US |
dc.subject | Paraphrase Detection | en_US |
dc.subject | Natural Language Processing (NLP) | en_US |
dc.subject | Deep Learning (DL) | en_US |
dc.title | Deep Extractive Text Summarization | en_US |
dc.type | Article | en_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.