Topic Segmentation
Topic segmentation aims to divide text or speech into coherent thematic units, facilitating improved understanding and downstream tasks like summarization and information retrieval. Current research emphasizes developing robust models, including those leveraging pretrained language and speech encoders, hierarchical clustering, and multi-task learning approaches, to handle diverse data types such as news broadcasts, dialogues, and research articles. This work addresses challenges like noisy data, varying levels of text structure, and the need for efficient segmentation of long documents, ultimately improving the analysis and organization of large textual and spoken corpora. The resulting advancements have significant implications for various applications, including improved document organization, enhanced dialogue systems, and more effective information retrieval.