Text summarisation plays a pivotal role in efficiently processing large volumes of textual data, making it an indispensable tool across diverse domains such as healthcare, legal, education, and journalism. It addresses the challenge of information overload by condensing or generating concise, meaningful summaries that improve decision-making, enhance accessibility, and save valuable time. Advances in artificial intelligence continue to propel the growth of text summarisation research, particularly with the evolution from traditional extractive approaches to cutting-edge abstractive models like BERT and GPT, as well as emerging innovations in multimodal and multilingual summarisation. To trace the development of this field, this study integrates bibliometric analysis and an in-depth survey, leveraging data from the Web of Science database to explore citation trends, uncover influential contributors, and highlight emerging research areas. Furthermore, bibliometric and critical evaluations are employed to outline strategic pathways and propose future directions for the continued advancement of the field. By incorporating sophisticated visualisation tools such as VOSviewer and RawGraphs, the analysis provides an enriched understanding of the field’s trajectory, identifying significant methodologies, landmark contributions, and existing gaps. This comprehensive exploration not only underscores the progress achieved in text summarisation but also serves as an invaluable resource for shaping forthcoming research endeavours and inspiring innovation in this dynamic area of study.