ORIGINAL RESEARCH article
Front. Artif. Intell.
Sec. Natural Language Processing
This article is part of the Research TopicThe Use of Large Language Models to Automate, Enhance, and Streamline Text Analysis Processes. Large Language Models Used to Analyze and Check Requirement Compliance.View all 4 articles
Text Summarization Method of Argumentative Discourse by Combining the BERT-Transformer Model
Provisionally accepted
- 1University of Hail, Ha'il, Saudi Arabia
- 2New Valley University, Kharga, Egypt
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Extractive and abstractive summarization techniques have recently gained significant attention. Each has its own limitations that reduce efficiency in the coverage of the main points of the summary, but by combining them, we can use the positive points of each to improve both summarization performance and summary generation quality. This paper presents a novel extractive-abstractive text summarization method that ensures coverage of the main points of the entire text. It is based on combining Bidirectional Encoder Representations from Transformers (BERT) and transfer learning. Using a dataset comprising two UK parliamentary debates, the study shows that the proposed method effectively summarizes the main points. Comparing extractive and abstractive summarization, the experiment used Recall-Oriented Understudy for Gisting Evaluation (ROUGE) sets of metrics and achieved scores of 30.1, 9.60, and 27.9 for the first debate, and 36.2, 11.80, and 31.5 for the second, using ROUGE-1, ROUGE-2, and ROUGE-L metrics, respectively.
Keywords: Extractive text summarization, Abstractive text summarization, bidirectional encoder representations from transformers (BERT), Transformer model, Argumentative discourse
Received: 08 Jul 2025; Accepted: 10 Nov 2025.
Copyright: © 2025 Altameemi, Altamimi, Alkhalil, Uliyan and Mansour. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Yaser Altameemi, y.albakry@uoh.edu.sa
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