Wed, Jul 9, 2025
[Archive]
Volume 17, Issue 1 (1-2025)
itrc 2025, 17(1): 27-35 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Rabiei M. Extractive Text Summarization in Persian Language through Integrative Approaches. itrc 2025; 17 (1) :27-35
URL: http://ijict.itrc.ac.ir/article-1-663-en.html
URL: http://ijict.itrc.ac.ir/article-1-663-en.html
Department of the Electrical and Computer Engineering, University of Eyvanekey, Semnan, Iran , mohammad.rabiei.uniud.it
Abstract: (257 Views)
Text summarization is the process of condensing a source text while retaining its key points, tailored to a specific audience or task. The research extractive summarization, where each news article was segmented into individual sentences. Each sentence underwent processing through the ParsBERT algorithm. Subsequently, an attention layer combined the sentence weights with the Bidirectional GRU algorithm's output to extract summarized sentences for labeling. The dataset comprised over 175,000 articles sourced from reputable Persian news agencies (ISNA-TASNIM), covering various topics such as science, politics, and sports. Evaluation of the summarization techniques was conducted using Rouge metrics. The results of the investigation revealed precision values of 0.7923 (Rouge-1), 0.7613 (Rouge-2), and 0.8582 (Rouge-L). The study also evaluated the effectiveness of Gated Recurrent Unit (GRU) algorithms in extractive summarization by integrating its architecture with the attention network. The results demonstrated an improvement in news text summarization compared to other deep learning hybrid algorithms.
Keywords: text summarization, Persian news, deep learning, attention network, extractive summarization
Type of Study: Research |
Subject:
Information Technology
Send email to the article author
| Rights and permissions | |
 | This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
