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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391
Title: Deep Extractive Text Summarization
Authors: Sharma, Yashvardhan
Keywords: Computer Science
Extractive Text Summarization
Paraphrase Detection
Natural Language Processing (NLP)
Deep Learning (DL)
Issue Date: 2020
Publisher: Elsevier
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.
URI: https://www.sciencedirect.com/science/article/pii/S1877050920306566
http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391
Appears in Collections:Department of Computer Science and Information Systems

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