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.

自动对话摘要是一个已经建立良好的任务，旨在从人类对话中识别出最重要的内容，创建一个简短的文本摘要。尽管该领域最近取得了进展，但我们发现大部分研究都集中在总结事实信息上，忽略了能够传达有用信息以分析、监控或支持人类互动的情感内容。本文提出并评估了一组度量标准 $PEmo$，以量化对话摘要中保留的情感程度。结果表明，现有的摘要模型在摘要中并不能很好地保留情感内容。我们还展示了通过将训练集仅限于情感对话，生成的摘要可以更好地保留情感内容，同时保持最突出的事实信息。