多视角融合的无监督对话主题分割模型OA

An unsupervised dialogue topic segmentation model based on multi-view fusion is proposed in this paper,which jointly models semantic similarity,logical coherence and summary consistency.The proposed framework adaptively integrates information from multiple perspectives through a hybrid mechanism combining static weighting with dynamic gating.Furthermore,a unified optimization objective is established by combi-ning neighboring utterance matching loss,summary consistency loss,and semantic-correlation modeling loss.Experimental results on three representative datasets show that the proposed model consistently achieves supe-rior performance,effectively improving robustness and global semantic coherence.