{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/investigating-gender-bias-in-news","title":"Bias in News Summarization: Measures, Pitfalls and Corpora","arxiv_id":"2309.08047","date":"2023-09-14","proceeding":null,"authors":["Julius Steen","Katja Markert"],"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.","url_abs":"https://arxiv.org/abs/2309.08047v3","url_pdf":"https://arxiv.org/pdf/2309.08047v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"investigating-gender-bias-in-news","repo_url":"https://github.com/julmaxi/summary_bias","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"document-summarization","task_name":"Document Summarization"},{"task_slug":"news-summarization","task_name":"News Summarization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2309.08047","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.08047"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/julmaxi/summary_bias","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"551a555db887134f","entry":"format_text","repo":"julmaxi/summary_bias","repo_kind":"official","path":"summarybias/instancegen/generate.py","file_url":"https://github.com/julmaxi/summary_bias/blob/HEAD/summarybias/instancegen/generate.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"551a555db887134f"}},{"code_sha256_prefix":"f1bbc88c00415179","entry":"parse_name_config","repo":"julmaxi/summary_bias","repo_kind":"official","path":"summarybias/instancegen/generate.py","file_url":"https://github.com/julmaxi/summary_bias/blob/HEAD/summarybias/instancegen/generate.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"f1bbc88c00415179"}},{"code_sha256_prefix":"9406d407b8853a38","entry":"parse_gender_config","repo":"julmaxi/summary_bias","repo_kind":"official","path":"summarybias/instancegen/generate.py","file_url":"https://github.com/julmaxi/summary_bias/blob/HEAD/summarybias/instancegen/generate.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"9406d407b8853a38"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}