A Systematic Review and Experimental Evaluation of Classical and Transformer-Based Models for Arabic Extractive Text Summarization
DOI:
https://doi.org/10.29304/jqcsm.2025.17.42562Keywords:
Arabic text summarization; Extractive text summarization; Abstractive summarization; Natural language processing; Transformer models; Arabic NLP.Abstract
Arabic text summarization has become an active research area due to the rapid growth of Arabic digital content. developing effective summarization models has many challenges result from the linguistic richness, the complex morphology, flexible syntax, and diverse writing styles. This study looks at Arabic extractive summarization, where the goal is to select the most relevant sentences from a text to create a concise version that still captures the original meaning. Many techniques were analyzed and synthesized with the existing data sets in addition to identifying the problems and gaps to understand the history of the given area and determine the direction of the research. Also, the metrics that used to evaluate the outcomes of the Arabic text summarization are mentioned. The review pointed out an evident development of classical statistical and graph-based extractive procedures to current transformer-based procedures.
Transformer architectures have been quickly embraced by the field and Arabic-specific pre-trained models have proven to perform better than multilingual counterparts. Nevertheless, there are still great gaps in multi-document summarization, dialect management and formalized evaluation systems. The research work that is to be done in the future is the creation of bigger Arabic corpora, better dialectal coverage, and creation of full-fledged evaluation standards.
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