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Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization

The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurd...

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Main Authors: Wahab, Muhammad Hafizul Hazmi, Abdul Hamid, Nor Asilah Wati, Subramaniam, Shamala, Latip, Rohaya, Othman, Mohamed
Format: Article
Published: Elsevier Ltd 2024
Online Access:http://psasir.upm.edu.my/id/eprint/105820/
https://www.sciencedirect.com/science/article/pii/S1568494623010128
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spelling my.upm.eprints.1058202024-04-05T03:32:13Z http://psasir.upm.edu.my/id/eprint/105820/ Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization Wahab, Muhammad Hafizul Hazmi Abdul Hamid, Nor Asilah Wati Subramaniam, Shamala Latip, Rohaya Othman, Mohamed The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores. Elsevier Ltd 2024-01 Article PeerReviewed Wahab, Muhammad Hafizul Hazmi and Abdul Hamid, Nor Asilah Wati and Subramaniam, Shamala and Latip, Rohaya and Othman, Mohamed (2024) Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization. Applied Soft Computing, 151. art. no. 110994. pp. 1-18. ISSN 1568-4946; ESSN: 1872-9681 https://www.sciencedirect.com/science/article/pii/S1568494623010128 10.1016/j.asoc.2023.110994
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description The central challenge in Automatic Text Summarization (ATS) involves efficiently generating machine-generated text summaries through optimization algorithms. An ATS is a critical component for systems dealing with textual information processing. However, the current approach faces a significant hurdle due to the computational intensity of the process, particularly when employing complex optimization techniques like swarm intelligence optimization alongside a costly ATS repair operator. While the current approach yields impressive Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metrics for the generated summary, it struggles with inefficiencies, mainly attributed to the substantial optimization time consumed by the ATS repair operator scheme. In order to address this, a novel solution called Decomposition-based Multi-Objective Differential Evolution (MODE/D) is proposed. It is built upon the foundation of Differential Evolution for Multi-Objective Optimization (DEMO) and the weighted sum method (WS), coupled with an innovative ATS repair operator scheme. Through experimentation on Document Understanding Conferences (DUC) datasets, the novel approach of MODE/D – WS is validated by evaluating the results using ROUGE metrics. The outcomes are twofold: a remarkable reduction in serial execution time and a noteworthy enhancement over existing techniques in the scholarly domain, as evidenced by improved ROUGE-1, ROUGE-2, and ROUGE-L scores.
format Article
author Wahab, Muhammad Hafizul Hazmi
Abdul Hamid, Nor Asilah Wati
Subramaniam, Shamala
Latip, Rohaya
Othman, Mohamed
spellingShingle Wahab, Muhammad Hafizul Hazmi
Abdul Hamid, Nor Asilah Wati
Subramaniam, Shamala
Latip, Rohaya
Othman, Mohamed
Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
author_facet Wahab, Muhammad Hafizul Hazmi
Abdul Hamid, Nor Asilah Wati
Subramaniam, Shamala
Latip, Rohaya
Othman, Mohamed
author_sort Wahab, Muhammad Hafizul Hazmi
title Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
title_short Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
title_full Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
title_fullStr Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
title_full_unstemmed Decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
title_sort decomposition–based multi-objective differential evolution for extractive multi-document automatic text summarization
publisher Elsevier Ltd
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/105820/
https://www.sciencedirect.com/science/article/pii/S1568494623010128
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score 13.211869

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