This paper investigates the impact of domain specificity on abstractive summarisation of Arabic financial texts using large language models (LLMs). We introduce AraFinNews, the largest publicly available Arabic financial news dataset to date, comprising 212,500 article--headline pairs spanning nearly a decade of reporting from October 2015 to July 2025. Designed as the Arabic equivalent of major English summarisation corpora such as CNN/DailyMail, AraFinNews provides a robust benchmark for evaluating domain-specific language understanding and generation in financial contexts. Using this resource, we evaluate transformer-based models -- including mT5, AraT5, and the domain-adapted FinAraT5 -- to examine how financial-domain pretraining influences factual accuracy, numerical reliability, and stylistic alignment with professional reporting. Experimental results show that domain-adapted models generate more faithful and coherent summaries, particularly in handling quantitative and entity-centric information. The findings highlight the importance of domain-specific adaptation for improving factual consistency and narrative fluency in Arabic financial summarisation. The dataset is freely available for non-commercial research atthis https URL.
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