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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
Title: ATSSI: Abstractive Text Summarization Using Sentiment Infusion
Authors: Sharma, Yashvardhan
Keywords: Computer Science
Abstractive Summarization
Condensed Text
Data Redundancy
Sentiment Analysis
Text Summarization
Issue Date: 2016
Publisher: Elsevier
Abstract: Text 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.
URI: https://www.sciencedirect.com/science/article/pii/S187705091631153X
http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8214
Appears in Collections:Department of Computer Science and Information Systems

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