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ATSSI: Abstractive Text Summarization Using Sentiment Infusion
- Source :
- Procedia Computer Science. :404-411
- Publisher :
- Published by Elsevier B.V.
-
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.
- Subjects :
- Computer science
Text graph
02 engineering and technology
Condensed Text
computer.software_genre
Summary
Text mining
020204 information systems
Multi-document summarization
0202 electrical engineering, electronic engineering, information engineering
Sentiment Analysis
Social media
General Environmental Science
Abstraction (linguistics)
Information retrieval
business.industry
Sentiment analysis
Automatic summarization
Abstractive Summarization
Data Redundancy
Text Summarization
General Earth and Planetary Sciences
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Natural language processing
Subjects
Details
- Language :
- English
- ISSN :
- 18770509
- Database :
- OpenAIRE
- Journal :
- Procedia Computer Science
- Accession number :
- edsair.doi.dedup.....0ecad049b47c300a8c37ea452b9cffa8
- Full Text :
- https://doi.org/10.1016/j.procs.2016.06.088