利用口语理解任务提升人与人对话摘要的忠实度
Increasing faithfulness in human-human dialog summarization with Spoken Language Understanding tasks
AI总结：
针对对话摘要模型易产生不一致内容、忠实度不足的问题，本研究将口语理解的语义信息融入面向任务的人与人对话摘要，提出新评估标准与标注数据集，实验验证该方法可提升摘要准确率。
摘要：
对话摘要旨在为多位说话者之间的对话提供简洁连贯的摘要。尽管语言模型的最新进展优化了这一过程，但由于需要理解说话者互动并捕捉相关信息，准确且忠实地总结对话仍然具有挑战性。事实上，用于对话摘要的生成式模型可能会生成包含不一致内容的摘要。我们提出，将面向任务的人机对话系统中用于执行口语理解（Spoken Language Understanding, SLU）的语义信息应用于面向任务的人与人对话，以获得在任务层面语义更忠实的摘要。本研究有三项核心贡献：第一，我们探究了引入任务相关信息如何优化摘要流程，从而生成语义更准确的摘要；第二，我们提出了一种基于任务语义的新评估标准；第三，我们发布了一个新的数据集版本，包含更多标准化标注数据，可供面向任务的对话摘要研究使用。本研究使用DECODA语料库（法国呼叫中心的口语对话合集）评估这些方法。结果表明，即使在词错误率不同的情况下，将模型与任务相关信息融合也能提升摘要准确率。
英文摘要：
Dialogue summarization aims to provide a concise and coherent summary of conversations between multiple speakers. While recent advancements in language models have enhanced this process, summarizing dialogues accurately and faithfully remains challenging due to the need to understand speaker interactions and capture relevant information. Indeed, abstractive models used for dialog summarization may generate summaries that contain inconsistencies. We suggest using the semantic information proposed for performing Spoken Language Understanding (SLU) in human-machine dialogue systems for goal-oriented human-human dialogues to obtain a more semantically faithful summary regarding the task. This study introduces three key contributions: First, we propose an exploration of how incorporating task-related information can enhance the summarization process, leading to more semantically accurate summaries. Then, we introduce a new evaluation criterion based on task semantics. Finally, we propose a new dataset version with increased annotated data standardized for research on task-oriented dialogue summarization. The study evaluates these methods using the DECODA corpus, a collection of French spoken dialogues from a call center. Results show that integrating models with task-related information improves summary accuracy, even with varying word error rates.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
