Arabic Abstractive Text Summarization Using an Ant Colony System

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.