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PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization

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Yongxin Zhou, Fabien Ringeval, François Portet

Computation and Language · Submitted 23 Jul 2023

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Our findings indicate that state-of-the-art summarization models do not preserve well the affective content within their summaries.

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arXiv ID2307.12371 (version 2)
Pith IDMUPDBPTT, Pith's permanent record of this paper and its review
CitationFabien Ringeval, Fran\c{c}ois Portet, Yongxin Zhou. "PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization." arXiv:2307.12371 (2023). https://pith.science/paper/MUPDBPTT
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,311 input tokens.

What the abstract itself states, as probabilities: claims first 0.07 · states limitation 0.88 · human participants 0.16 · controlled comparison 0.25 · randomized assignment 0.02 · states quantitative result 0.07 · compares with prior results 0.77 · mentions code or data availability 0.03.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 Automatic dialogue summarization is a well-established task with the goal of distilling the most crucial information from human conversations into concise textual summaries. background 1.00 0.03 0.02 asserted 1.00
s1 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. gap 0.99 0.06 0.04 asserted 0.98
s2 In this paper, we introduce and assess a set of measures PSentScore, aimed at quantifying the preservation of affective content in dialogue summaries. contribution 0.99 0.95 0.07 asserted 0.99
s3 Our findings indicate that state-of-the-art summarization models do not preserve well the affective content within their summaries. result 1.00 0.94 0.60 asserted 0.60
s4 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. result 0.99 0.96 0.19 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.90 · evidence from observation or experiment 0.97.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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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