Automatic dialogue summarization is a well-established task with the goal of distilling the most crucial information from human conversations into concise textual summaries. However, most existing research has predominantly focused on summarizing factual information, neglecting the affective content, which can hold valuable insights for analyzing, monitoring, or facilitating human interactions. In this paper, we introduce and assess a set of measures PSentScore, aimed at quantifying the preservation of affective content in dialogue summaries. Our findings indicate that state-of-the-art summarization models do not preserve well the affective content within their summaries. Moreover, we demonstrate that a careful selection of the training set for dialogue samples can lead to improved preservation of affective content in the generated summaries, albeit with a minor reduction in content-related metrics.

翻译：自动对话摘要是一项成熟的任务，旨在将人类对话中最关键的信息提炼为简洁的文本摘要。然而，现有研究大多侧重于总结事实信息，忽视了情感内容，而情感内容在分析、监控或促进人际互动中可能蕴含宝贵洞见。本文提出并评估了一套度量体系PSentScore，旨在量化对话摘要中情感内容的保留程度。研究结果表明，最先进的摘要模型在其生成的摘要中未能良好保留情感内容。此外，我们证明，精心选择对话样本的训练集可以改善生成摘要中情感内容的保留，尽管这会导致内容相关指标略有下降。