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Please use this identifier to cite or link to this item: http://dspace.bits-pilani.ac.in:8080/jspui/handle/123456789/8214
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dc.contributor.authorSharma, Yashvardhan-
dc.date.accessioned2023-01-02T10:05:45Z-
dc.date.available2023-01-02T10:05:45Z-
dc.date.issued2016-
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S187705091631153X-
dc.identifier.urihttp://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8214-
dc.description.abstractText Summarization is condensing of text such that, redundant data are removed and important information is extracted and represented in the shortest way possible. With the explosion of the abundant data present on social media, it has become important to analyze this text for seeking information and use it for the advantage of various applications and people. From past few years, this task of automatic summarization has stirred the interest among communities of Natural Language Processing and Text Mining, especially when it comes to opinion summarization. Opinions play a pivotal role in decision making in the society. Other's opinions and suggestions are the base for an individual or a company while making decisions. In this paper, we propose a graph based technique that generates summaries of redundant opinions and uses sentiment analysis to combine the statements. The summaries thus generated are abstraction based summaries and are well formed to convey the gist of the text.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectComputer Scienceen_US
dc.subjectAbstractive Summarizationen_US
dc.subjectCondensed Texten_US
dc.subjectData Redundancyen_US
dc.subjectSentiment Analysisen_US
dc.subjectText Summarizationen_US
dc.titleATSSI: Abstractive Text Summarization Using Sentiment Infusionen_US
dc.typeArticleen_US
Appears in Collections:Department of Computer Science and Information Systems

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