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.

翻译：对话主题分割（DTS）对于理解任务导向的公共信道通信（如海事甚高频对话）至关重要，此类对话具有非正式语音和隐式主题转换的特点。为克服传统方法的局限，我们提出了DASH-DTS——一种基于大语言模型的新型框架。其核心贡献包括：（1）通过对话握手识别实现主题转移检测；（2）借助相似性引导的示例选择进行上下文增强；（3）生成选择性正负样本以提升模型判别力与鲁棒性。此外，我们发布了首个真实世界海事甚高频通信公开数据集VHF-Dial，以推动该领域研究。DASH-DTS为每个分割段提供可解释的推理过程及置信度评分。实验结果表明，该框架在VHF-Dial数据集及标准基准测试中均实现了多项最先进的分割可信精度，为操作对话的稳定监控与决策支持奠定了坚实基础。