Open Access

ARTICLE

Automatic Text Summarization Using Genetic Algorithm and Repetitive Patterns

by Ebrahim Heidary¹, Hamïd Parvïn^2,3,4,*, Samad Nejatian^5,6, Karamollah Bagherifard^1,6, Vahideh Rezaie^6,7, Zulkefli Mansor⁸, Kim-Hung Pho⁹

1 Department of Computer Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
2 Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam
3 Faculty of Information Technology, Duy Tan University, Da Nang, 550000, Vietnam
4 Department of Computer Science, Nourabad Mamasani Branch, Islamic Azad University, Mamasani, Iran
5 Department of Electrical Engineering, Yasooj Branch, Islamic Azad University, Yasooj, Iran
6 Young Researchers and Elite Club, Yasooj Branch, Islamic Azad University, Yasooj, Iran
7 Department of Mathematics, Yasooj Branch, Islamic Azad University, Yasooj, Iran
8 Fakulti Teknologi dan Sains Maklumat, Universiti Kebangsan Malaysia, 43600 UKM Bangi, Selangor, Malaysia
9 Fractional Calculus, Optimization and Algebra Research Group, Faculty of Mathematics and Statistics, Ton Duc Thang University, Ho Chi Minh City, Vietnam

* Corresponding Author: Hamïd Parvïn. Email:

Computers, Materials & Continua 2021, 67(1), 1085-1101. https://doi.org/10.32604/cmc.2021.013836

Received 30 August 2020; Accepted 14 September 2020; Issue published 12 January 2021

Abstract

Taking into account the increasing volume of text documents, automatic summarization is one of the important tools for quick and optimal utilization of such sources. Automatic summarization is a text compression process for producing a shorter document in order to quickly access the important goals and main features of the input document. In this study, a novel method is introduced for selective text summarization using the genetic algorithm and generation of repetitive patterns. One of the important features of the proposed summarization is to identify and extract the relationship between the main features of the input text and the creation of repetitive patterns in order to produce and optimize the vector of the main document features in the production of the summary document compared to other previous methods. In this study, attempts were made to encompass all the main parameters of the summary text including unambiguous summary with the highest precision, continuity and consistency. To investigate the efficiency of the proposed algorithm, the results of the study were evaluated with respect to the precision and recall criteria. The results of the study evaluation showed the optimization the dimensions of the features and generation of a sequence of summary document sentences having the most consistency with the main goals and features of the input document.

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Keywords

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