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Data Text Mining Based on Swarm Intelligence Techniques: Review of Text Summarization Systems

Data Text Mining Based on Swarm Intelligence Techniques: Review of Text Summarization Systems
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Author(s): Mohamed Atef Mosa (Institute of Public Administration, Department of Information Technology, Riyadh, Saudi Arabia)
Copyright: 2020
Pages: 37
Source title: Trends and Applications of Text Summarization Techniques
Source Author(s)/Editor(s): Alessandro Fiori (Candiolo Cancer Institute – FPO, IRCCS, Italy)
DOI: 10.4018/978-1-5225-9373-7.ch004

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Abstract

Due to the great growth of data on the web, mining to extract the most informative data as a conceptual brief would be beneficial for certain users. Therefore, there is great enthusiasm concerning the developing automatic text summary approaches. In this chapter, the authors highlight using the swarm intelligence (SI) optimization techniques for the first time in solving the problem of text summary. In addition, a convincing justification of why nature-heuristic algorithms, especially ant colony optimization (ACO), are the best algorithms for solving complicated optimization tasks is introduced. Moreover, it has been perceived that the problem of text summary had not been formalized as a multi-objective optimization (MOO) task before, despite there are many contradictory objectives in needing to be achieved. The SI has not been employed before to support the real-time tasks. Therefore, a novel framework of short text summary has been proposed to fulfill this issue. Ultimately, this chapter will enthuse researchers for further consideration for SI algorithms in solving summary tasks.

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