<p>A detailed bibliometric analysis of text summarization research from 2000 to 2024 is performed in this study. It tracks the growth of the field, identifies key contributors, and examines collaborative networks that have shaped the development of text summarization technologies. The analysis shows a significant rise in publication volume, particularly in the last decade, driven by advancements in natural language processing and AI. China, India, and the USA emerge as major contributors, with notable institutions and researchers leading the field. Research themes have evolved from foundational topics to advanced AI-driven approaches like deep learning and abstractive summarization. The study also highlights the global and collaborative nature of research, with extensive partnerships across institutions and countries. The findings offer valuable insights into the current state and future directions of text summarization research, providing a solid foundation for further exploration and development. Data are gathered from major academic databases like Scopus, ACM, and IEEE Xplore and filtered through a rigorous selection process to create a comprehensive dataset. Bibliometric methods analyzed publication trends, key sources, and geographical contributions. The structural topic model identified principal research themes, while the Mann–Kendall test examined trends in these themes over time. Social network analysis visualized collaborations among scholars and institutions. Key research themes include multihead attention mechanisms, graph-based semantic analysis, and topic modeling techniques, with emerging trends highlighting interest in self-supervised learning, zero-shot learning, and transformer models. This study offers valuable insights and serves as a useful resource for researchers and practitioners, enhancing the understanding of current and future directions in text summarization.</p>