Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
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Computer Science > Computation and Language
Title:Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
Abstract:Improving the quality of model-generated summaries, especially factuality, the accuracy of a summary with respect to its source content, remains a challenge. While reranking could select the optimal output from multiple generated candidates, it is limited to only using the source as guidance, resulting in unreliable summaries. To address this limitation, we propose ConSUM that reranks candidate summaries by considering two factors: consistency to the source document and consensus among the other candidates. Consensus is established using Minimum Bayes Risk (MBR) decoding over the set of generated summaries, while ensuring consistency by employing factuality-aware metrics that compare the summary against the source. Rigorous testing demonstrates that our system is competitive with existing methods, with human evaluations further confirming that its generated summaries are preferred over those from other systems. Our code is available at this https URL .
| Comments: | Accepted to ACL 2026 Findings |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.29336 [cs.CL] |
| (or arXiv:2605.29336v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.29336
arXiv-issued DOI via DataCite (pending registration)
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Submission history
From: Riza Setiawan Soetedjo [view email][v1] Thu, 28 May 2026 04:14:43 UTC (1,500 KB)
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