Deep Extractive Text Summarization

dc.contributor.authorSharma, Yashvardhan
dc.date.accessioned2024-11-15T09:05:50Z
dc.date.available2024-11-15T09:05:50Z
dc.date.issued2020
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.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1877050920306566
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/16391
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

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