Personalised News Summarisation Using Demographic-aware BART Model - McFadyen, Daniel M.; Mahmud, Redowan; Afrin, Mahbuba; Mistry, Sajib; Krishna, Aneesh (2025)
The exponential growth of digital media has led to a saturation of online news content, making it increasingly important to deliver information that resonates with readers’ specific backgrounds and interests. While traditional summarisation methods treat all audiences homogeneously, recent advances highlight the need for demographic-aware approaches. However, existing automated news summarising systems rarely incorporate these nuances, often defaulting to one-size-fits-all summaries. Therefore, this research introduces a novel frameworkforpersonalised newssummarisation that accounts for reader-specific factors such as age, geography, and cultural background. We developed a specialised dataset linking demographic attributes to narrative priorities and used this to finetune an open-source BART model for dynamic, audience-aligned information emphasis. Integrated into a user-friendly web application, this model enables the generation of tailored summaries that better meet individual preferences. Comparative evaluations reveal that our approach substantially surpasses traditional summarisation tools, including leading models like ChatGPT 3.5 and Gemini, by providing more contextually relevant content.