@inproceedings{soetedjo-etal-2026-enhancing,
title = "Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum {B}ayes Risk Decoding",
author = "Soetedjo, Riza Setiawan and
Sakai, Yusuke and
Kamigaito, Hidetaka and
Kwon, Jingun and
Okumura, Manabu and
Watanabe, Taro",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.2071/",
pages = "41686--41713",
ISBN = "979-8-89176-395-1",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
%A Soetedjo, Riza Setiawan
%A Sakai, Yusuke
%A Kamigaito, Hidetaka
%A Kwon, Jingun
%A Okumura, Manabu
%A Watanabe, Taro
%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 soetedjo-etal-2026-enhancing
%X 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.
%U https://aclanthology.org/2026.findings-acl.2071/
%P 41686-41713
Markdown (Informal)
[Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding](https://aclanthology.org/2026.findings-acl.2071/) (Soetedjo et al., Findings 2026)
ACL