Dialogue Segmentation

Dialogue segmentation aims to automatically divide conversations into coherent topic segments, facilitating improved understanding and analysis of spoken or written interactions. Current research focuses on both supervised and unsupervised approaches, employing techniques like utterance rewriting, hyperdimensional computing, and structured prompting with large language models to enhance segmentation accuracy. These advancements are crucial for various applications, including improved dialogue systems, more effective information retrieval from conversational data, and a deeper understanding of discourse structure in diverse contexts.