Automatic dialogue summarization is a well-established task that aims to identify the most important content from human conversations to create a short textual summary. Despite recent progress in the field, we show that most of the research has focused on summarizing the factual information, leaving aside the affective content, which can yet convey useful information to analyse, monitor, or support human interactions. In this paper, we propose and evaluate a set of measures $PEmo$, to quantify how much emotion is preserved in dialog summaries. Results show that, summarization models of the state-of-the-art do not preserve well the emotional content in the summaries. We also show that by reducing the training set to only emotional dialogues, the emotional content is better preserved in the generated summaries, while conserving the most salient factual information.

翻译：自动对话摘要是成熟的自然语言处理任务，旨在从人类对话中提取核心内容生成简短文本摘要。尽管该领域近期取得进展，但我们发现现有研究主要聚焦于事实信息摘要，忽视了可能对分析、监控或支持人际交互具有重要价值的情感内容。本文提出并评估了一组度量指标$PEmo$，用于量化对话摘要中情感保留程度。实验表明，当前最先进的摘要模型未能良好保留原文情感内容。通过将训练集缩减为仅含情感对话的数据，模型生成摘要的情感保留度得到显著提升，同时仍能保持最突出的事实信息。