Enhanced continuous and discrete multi objective particle swarm optimization for text summarization

Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So...

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Bibliographic Details
Published in:Cluster computing 2019-01, Vol.22 (Suppl 1), p.229-240
Main Authors: Priya, V., Umamaheswari, K.
Format: Article
Language:English
Subjects:
Algorithms
Computer Communication Networks
Computer Science
Data mining
Datasets
Decision analysis
Efficiency
Entropy
Feature selection
Literature reviews
Mutation
Operating Systems
Optimization techniques
Particle swarm optimization
Processor Architectures
Semantics
Summaries
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Summary:Reviews from various domains is being posted in web increasingly day by day. Analyzing this enormous content would be useful in decision making for various stakeholders. Text summarization techniques generate concise summaries including sentiments which are useful in analyzing the large content. So text summarization systems become significant in analyzing this huge content. The summaries are generated based on important features using multi objective approaches where sufficient literature is not available. Major limitations of text summarization systems are scalability and performance. Two variants of multi objective optimization techniques such as Discrete and Continuous which work under the principles of particle swarm optimization (PSO) for extractive summarization of reviews had been proposed for performance improvement. The performance is validated using Recall-Oriented Understanding for Gisting Evaluation (ROUGE), Success Counting (SC) and Inverted Generational Distance (IGD). Based on the experimental results it is found that the system is effective using multi-objective PSO algorithm when compared to other state-of-art approaches like Liu’s approach feature based binary particle swarm optimization and etc. for feature based review summarization.
ISSN:1386-7857
1573-7543
DOI:10.1007/s10586-018-2674-1