A Review of Arabic Text Summarization: Methods, Datasets, and Evaluation Metrics, With a Proposed Solution

Abstract

This survey comprehensively reviews Arabic text summarization, examining state-of-the-art methodologies, commonly used datasets, and evaluation practices. Despite notable progress, the field faces challenges such as fragmented benchmarking, inconsistent metric use, and lacking resources for long-document summarization. We categorize existing summarization methods into traditional, Transformer-based, and hybrid approaches, highlighting their strengths and limitations. We introduce Mukhtasar, a novel dataset supporting short and long summaries across diverse genres to address significant gaps. Additionally, we propose six standardized evaluation splits tailored to distinct summarization goals, promoting reproducibility and fair comparison. To address inconsistencies, we also recommend a consistent reporting protocol using ROUGE-1, ROUGE-2, ROUGE-L, and ROUGE-S. While lexical overlap metrics dominate evaluation practices, we identify the absence of neural-based metrics for Arabic as a significant limitation and call for future development in this area. Our contributions aim to unify evaluation protocols, enrich available resources, and guide the community toward more interpretable and scalable Arabic summarization research.