使用以主题和参与者为中心的分解方法进行具有情感动态的对话摘要
Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition
- Delft University of Technology（代尔夫特理工大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
该研究针对对话摘要问题，提出基于改进分层智能体链方法的框架，从主题和参与者两个视角分解对话，结合自动推断情感生成摘要，并引入情感轨迹指标评估，实验表明其框架能生成含语义和情感内容的摘要，凸显方法有效性。
AI中文摘要：
现有文本摘要研究多聚焦于独白信息（如报纸文章、报告），未考虑说话者或作者间的互动。而对话是丰富的交流渠道，多参与者来回交流构建意义。我们提出一个对话摘要框架，基于改进的分层智能体链方法，利用多模态对话输入明确建模语义和情感动态。从两个视角分解对话：基于所有参与者话语的主题片段和特定参与者的话语片段，用于生成包含自动推断情感的相应摘要。主题和参与者级摘要聚合为捕捉语义内容和情感轨迹的对话摘要。为评估内容准确性之外的情况，引入情感轨迹指标。在多模态对话数据集上用小语言模型进行的实验表明，我们的框架能生成兼具语义和情感内容的摘要。进一步实验突出了所提方法的有效性及使用语言模型进行对话分析的机会。
英文摘要：
Existing text summarization research has focused much on monologic information (e.g., newspaper articles, reports) without accounting for the interaction between speakers or authors. In contrast, dialogues are a rich communication channel where multiple participants conduct back and forth exchanges to construct meaning. We propose a dialogue summarization framework that explicitly models both semantic and emotion dynamics using multimodal dialogue inputs, built on an adapted hierarchical Chain-of-Agents approach. We decompose dialogues from two perspectives: (1) topic segments based on the utterances of all participants, and (2) participant-specific utterance segments. These are used to generate corresponding summaries while incorporating automatically inferred emotions. Topic- and participant-level summaries are aggregated into a dialogue summary capturing semantic content and emotion trajectories. To evaluate beyond content accuracy, we introduce emotion trajectory metrics measuring how well summaries preserve emotional flow. Experiments with small language models on multimodal dialogue datasets show that our framework produces summaries with both semantic and emotion content. Further experiments on explicit emotion label availability highlight the efficacy of our proposed methodology and the opportunities in dialogue analysis using language models.
