DASH: Dialogue-Aware Similarity and Handshake Recognition for Topic Segmentation in Public-Channel Conversations

• URL: http://arxiv.org/abs/2512.15042v1
• Date: Wed, 17 Dec 2025 03:13:10 GMT
• Title: DASH: Dialogue-Aware Similarity and Handshake Recognition for Topic Segmentation in Public-Channel Conversations
• Abstract summary: Dialogue Topic (DTS) is crucial for understanding task-oriented public-channel communications, such as maritime VHF dialogues.<n>To address the limitations of traditional methods, we propose DASH-DTS, a novel LLM-based framework.<n>We release VHF-Dial, the first public dataset of real-world maritime VHF communications, to advance research in this domain.
• Score: 4.73074147871828
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Dialogue Topic Segmentation (DTS) is crucial for understanding task-oriented public-channel communications, such as maritime VHF dialogues, which feature informal speech and implicit transitions. To address the limitations of traditional methods, we propose DASH-DTS, a novel LLM-based framework. Its core contributions are: (1) topic shift detection via dialogue handshake recognition; (2) contextual enhancement through similarity-guided example selection; and (3) the generation of selective positive and negative samples to improve model discrimination and robustness. Additionally, we release VHF-Dial, the first public dataset of real-world maritime VHF communications, to advance research in this domain. DASH-DTS provides interpretable reasoning and confidence scores for each segment. Experimental results demonstrate that our framework achieves several sota segmentation trusted accuracy on both VHF-Dial and standard benchmarks, establishing a strong foundation for stable monitoring and decision support in operational dialogues.

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