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Deep Extractive Text Summarization

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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


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