Arabic Abstractive Text Summarization Using an Ant Colony System

Amal M. Al-Numai and Aqil M. Azmi ()
Additional contact information
Amal M. Al-Numai: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia
Aqil M. Azmi: Department of Computer Science, College of Computer and Information Sciences, King Saud University, Riyadh 11543, Saudi Arabia

Mathematics, 2025, vol. 13, issue 16, 1-24

Abstract: Arabic abstractive summarization presents a complex multi-objective optimization challenge, balancing readability, informativeness, and conciseness. While extractive approaches dominate NLP, abstractive methods—particularly for Arabic—remain underexplored due to linguistic complexity. This study introduces, for the first time, ant colony system (ACS) for Arabic abstractive summarization (named AASAC—Arabic Abstractive Summarization using Ant Colony), framing it as a combinatorial evolutionary optimization task. Our method integrates collocation and word-relation features into heuristic-guided fitness functions, simultaneously optimizing content coverage and linguistic coherence. Evaluations on a benchmark dataset using LemmaRouge , a lemma-based metric that evaluates semantic similarity rather than surface word forms, demonstrate consistent superiority. For 30% summaries, AASAC achieves 51.61% ( LemmaRouge -1) and 46.82% ( LemmaRouge -L), outperforming baselines by 13.23% and 20.49%, respectively. At 50% summary length, it reaches 64.56% ( LemmaRouge -1) and 61.26% ( LemmaRouge -L), surpassing baselines by 10.73% and 3.23%. These results highlight AASAC’s effectiveness in addressing multi-objective NLP challenges and establish its potential for evolutionary computation applications in language generation, particularly for complex morphological languages like Arabic.

Keywords: summarization; abstractive summarization; ant colony system (ACS); LemmaRouge Metric; arabic language (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/16/2613/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/16/2613/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:16:p:2613-:d:1724908

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().