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        <title>Calibrating Model-Based Evaluation Metrics for Summarization</title>
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        <namePart type="given">Hongye</namePart>
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            <namePart type="given">Jiajun</namePart>
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    <abstract>Recent advances in summary evaluation are based on model-based metrics to assess quality dimensions, such as completeness, conciseness, and faithfulness. However, these methods often require large language models, and predicted scores are frequently miscalibrated, limiting their reliability. Moreover, evaluating the average quality across different summaries for a single document typically requires access to multiple reference summaries. Here, we propose a general framework that generates individual and average proxy scores without relying on reference summaries, human annotations, or expensive model-based metrics. We also propose group isotonic regression binning (GIRB), a calibration method that adjusts the raw predictions to better align with ground-truth evaluation metrics. While we focus on continuous-value scenarios, such as summarization, the method is applicable to discrete-value tasks, such as question answering. Experiments on seven datasets demonstrate that our approach consistently outperforms existing baselines.</abstract>
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%0 Conference Proceedings
%T Calibrating Model-Based Evaluation Metrics for Summarization
%A Liu, Hongye
%A Brahma, Dhanajit
%A Henao, Ricardo
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F liu-etal-2026-calibrating
%X Recent advances in summary evaluation are based on model-based metrics to assess quality dimensions, such as completeness, conciseness, and faithfulness. However, these methods often require large language models, and predicted scores are frequently miscalibrated, limiting their reliability. Moreover, evaluating the average quality across different summaries for a single document typically requires access to multiple reference summaries. Here, we propose a general framework that generates individual and average proxy scores without relying on reference summaries, human annotations, or expensive model-based metrics. We also propose group isotonic regression binning (GIRB), a calibration method that adjusts the raw predictions to better align with ground-truth evaluation metrics. While we focus on continuous-value scenarios, such as summarization, the method is applicable to discrete-value tasks, such as question answering. Experiments on seven datasets demonstrate that our approach consistently outperforms existing baselines.
%U https://aclanthology.org/2026.findings-acl.1760/
%P 35285-35315

Markdown (Informal)

[Calibrating Model-Based Evaluation Metrics for Summarization](https://aclanthology.org/2026.findings-acl.1760/) (Liu et al., Findings 2026)

• Calibrating Model-Based Evaluation Metrics for Summarization (Liu et al., Findings 2026)

ACL

• Hongye Liu, Dhanajit Brahma, and Ricardo Henao. 2026. Calibrating Model-Based Evaluation Metrics for Summarization. In Findings of the Association for Computational Linguistics: ACL 2026, pages 35285–35315, San Diego, California, United States. Association for Computational Linguistics.