Evaluating Emotional Nuances in Dialogue Summarization
Yongxin Zhou Fabien Ringeval François Portet
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.