Topic Transition

Topic transition in dialogue and text focuses on understanding and modeling how conversations and documents shift between subjects, aiming to improve the coherence and naturalness of AI-generated text and the accuracy of topic segmentation in various data types. Current research emphasizes developing models that leverage knowledge graphs, incorporate multiple levels of topic granularity, and utilize techniques like contrastive learning and attention mechanisms to better capture semantic relationships and predict topic shifts. This work is crucial for advancing the capabilities of dialogue systems, improving information retrieval from long documents, and enhancing the quality of text summarization and generation.