用共识与一致性提升摘要事实性，效果优于现有方法。
Enhancing Factuality through Consensus and Consistency in Summarization Using Minimum Bayes Risk Decoding
- 通过最小贝叶斯风险解码整合候选摘要的共识信息
- 结合源文档一致性与多候选间共识，显著提升事实准确率
- 适合需要高可信摘要的新闻/科研内容生成场景
提升模型生成摘要的质量，尤其是其事实性（即摘要与源文本的一致性），仍是挑战。虽然重排序可从多个生成候选中选出最优输出，但仅依赖源文档指导，易导致不可靠摘要。为此，我们提出ConSUM，通过考虑两个因素对候选摘要进行重排序：与源文档的一致性，以及候选间的共识。共识通过在生成摘要集合上使用最小贝叶斯风险（MBR）解码建立，同时通过事实性感知指标确保摘要与源文档的一致性。严格测试表明，该系统在性能上可与现有方法竞争，人工评估进一步证实其生成的摘要更受青睐。代码已开源：https://github.com/naist-nlp/ConSUM。
原文摘要 · Abstract (English)
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 https://github.com/naist-nlp/ConSUM .
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