PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization
arxiv(2023)
摘要
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.
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