# PSentScore: Evaluating Sentiment Polarity in Dialogue Summarization

arXiv: https://arxiv.org/abs/2307.12371 · HTML: https://mlsanity.com/paper/2307.12371/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 36 references that are in the map.
- First posted on arXiv 23 Jul 2023.

## Builds on (most cited first, 19 of 19)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization](https://mlsanity.com/paper/1911.12237/) (2019)
- [QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization](https://mlsanity.com/paper/2104.05938/) (2021)
- [DialogSum: A Real-Life Scenario Dialogue Summarization Dataset](https://mlsanity.com/paper/2105.06762/) (2021)
- [MediaSum: A Large-scale Media Interview Dataset for Dialogue Summarization](https://mlsanity.com/paper/2103.06410/) (2021)
- [What Have We Achieved on Text Summarization?](https://mlsanity.com/paper/2010.04529/) (2020)
- [Neural Network-Based Abstract Generation for Opinions and Arguments](https://mlsanity.com/paper/1606.02785/) (2016)
- [TWEETSUMM -- A Dialog Summarization Dataset for Customer Service](https://mlsanity.com/paper/2111.11894/) (2021)
- [CSDS: A Fine-Grained Chinese Dataset for Customer Service Dialogue Summarization](https://mlsanity.com/paper/2108.13139/) (2021)
- [EmailSum: Abstractive Email Thread Summarization](https://mlsanity.com/paper/2107.14691/) (2021)
- [Analyzing and Evaluating Faithfulness in Dialogue Summarization](https://mlsanity.com/paper/2210.11777/) (2022)
- [Questioning the Validity of Summarization Datasets and Improving Their Factual Consistency](https://mlsanity.com/paper/2210.17378/) (2022)
- [A Focused Study on Sequence Length for Dialogue Summarization](https://mlsanity.com/paper/2209.11910/) (2022)
- [Effectiveness of French Language Models on Abstractive Dialogue Summarization Task](https://mlsanity.com/paper/2207.08305/) (2022)
- [DialogSum Challenge: Results of the Dialogue Summarization Shared Task](https://mlsanity.com/paper/2208.03898/) (2022)

## Built on by (most cited first, 4 of 4)

- [Can GPT models Follow Human Summarization Guidelines? A Study for Targeted Communication Goals](https://mlsanity.com/paper/2310.16810/) (2023)
- [What Matters to an LLM? Behavioral and Computational Evidences from Summarization](https://mlsanity.com/paper/2602.00459/) (2026)
- [Automated Clinical Report Generation for Remote Cognitive Remediation: Comparing Knowledge-Engineered Templates and LLMs in Low-Resource Settings](https://mlsanity.com/paper/2605.06594/) (2026)
- [Dialogue Summarization with Emotion Dynamics Using Topic- and Participant-Centric Decomposition](https://mlsanity.com/paper/2607.14769/) (2026)

## Abstract (arXiv)

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.