In today’s world, where the amount of textual information generated by humans and machines is rapidly growing, computational methods for organizing, summarizing, and tracking textual information and its evolution are becoming increasingly important. This paper introduces ANTM, an algorithmic family of dynamic topic models that combines novel techniques for discovering evolving topics in large corpora. ANTM preserves the temporal continuity of evolving topics by extracting temporal features from documents with advanced pre-trained large language models and by employing an overlapping sliding window algorithm for sequential aligned document clustering. This clustering method identifies different numbers of topics within each time frame and aligns semantically similar document clusters across time periods. It captures emerging and fading topics over time and allows for a more diverse and coherent representation of evolving topics. We evaluate ANTM against four other dynamic topic models on three datasets and conclude that it outperforms the state-of-the-art approaches in terms of interpretability and diversity. Furthermore, we demonstrate its effectiveness in handling large corpora, while improving the scalability and adaptability of dynamic topic models to different domains.