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        <title>Bias in News Summarization: Measures, Pitfalls and Corpora</title>
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    <abstract>Summarization is an important application of large language models (LLMs). Most previous evaluation of summarization models has focused on their content selection, faithfulness, grammaticality and coherence. However, it is well known that LLMs can reproduce and reinforce harmful social biases. This raises the question: Do biases affect model outputs in a constrained setting like summarization?To help answer this question, we first motivate and introduce a number of definitions for biased behaviours in summarization models, along with practical operationalizations. Since we find that biases inherent to input documents can confound bias analysis in summaries, we propose a method to generate input documents with carefully controlled demographic attributes. This allows us to study summarizer behavior in a controlled setting, while still working with realistic input documents.We measure gender bias in English summaries generated by both purpose-built summarization models and general purpose chat models as a case study. We find content selection in single document summarization to be largely unaffected by gender bias, while hallucinations exhibit evidence of bias.To demonstrate the generality of our approach, we additionally investigate racial bias, including intersectional settings.</abstract>
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%0 Conference Proceedings
%T Bias in News Summarization: Measures, Pitfalls and Corpora
%A Steen, Julius
%A Markert, Katja
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F steen-markert-2024-bias
%X Summarization is an important application of large language models (LLMs). Most previous evaluation of summarization models has focused on their content selection, faithfulness, grammaticality and coherence. However, it is well known that LLMs can reproduce and reinforce harmful social biases. This raises the question: Do biases affect model outputs in a constrained setting like summarization?To help answer this question, we first motivate and introduce a number of definitions for biased behaviours in summarization models, along with practical operationalizations. Since we find that biases inherent to input documents can confound bias analysis in summaries, we propose a method to generate input documents with carefully controlled demographic attributes. This allows us to study summarizer behavior in a controlled setting, while still working with realistic input documents.We measure gender bias in English summaries generated by both purpose-built summarization models and general purpose chat models as a case study. We find content selection in single document summarization to be largely unaffected by gender bias, while hallucinations exhibit evidence of bias.To demonstrate the generality of our approach, we additionally investigate racial bias, including intersectional settings.
%U https://aclanthology.org/2024.findings-acl.356
%P 5962-5983

Markdown (Informal)

[Bias in News Summarization: Measures, Pitfalls and Corpora](https://aclanthology.org/2024.findings-acl.356) (Steen & Markert, Findings 2024)

• Bias in News Summarization: Measures, Pitfalls and Corpora (Steen & Markert, Findings 2024)

ACL

• Julius Steen and Katja Markert. 2024. Bias in News Summarization: Measures, Pitfalls and Corpora. In Findings of the Association for Computational Linguistics ACL 2024, pages 5962–5983, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.