通过信念层面聚合实现分歧下的忠实总结
Faithful Summarisation under Disagreement via Belief-Level Aggregation
AI总结：
本文提出一种通过信念层面聚合实现分歧意识的总结方法，结合结构化信念集和大语言模型生成，提升观点密集场景下的摘要忠实性。
摘要：
观点和多文档总结往往涉及真实冲突的观点，然而许多现有方法，特别是基于大语言模型的系统，会隐式地平滑分歧并过度代表多数意见。这限制了在观点密集的环境中生成的总结的忠实性。我们引入了一种意识到分歧的合成流程，将信念层面的聚合与语言生成分开。文档首先表示为结构化的信念集，并使用基于距离的信念合并运算符进行聚合，这些运算符显式地建模冲突。然后，大型语言模型仅用于将聚合的信念转化为自然语言摘要。我们评估了该方法在多个模型家族和规模上的表现，并将其与在生成过程中执行显式聚合的方法进行比较。我们的结果表明，虽然足够大的模型在聚合在生成过程中时可以匹配信念层面的聚合，但这种行为在不同架构或容量上并不稳定。相比之下，信念层面的聚合结合简单的提示生成，在不同模型上都能保持一致的分歧意识表现，同时保持流畅和扎根的摘要。
英文摘要：
Opinion and multi-document summarisation often involve genuinely conflicting viewpoints, yet many existing approaches, particularly LLM-based systems, implicitly smooth disagreement and over-represent majority opinions. This limits the faithfulness of generated summaries in opinion-heavy settings. We introduce a disagreement-aware synthesis pipeline that separates belief-level aggregation from language generation. Documents are first represented as structured belief sets and aggregated using distance-based belief merging operators that explicitly model conflict. Large language models are then used only to realise the aggregated beliefs as natural language summaries. We evaluate the approach across multiple model families and scales, comparing it to methods that perform explicit aggregation during generation. Our results show that while sufficiently large models can match belief-level aggregation when aggregation is handled at generation time, this behaviour is not stable across architectures or capacities. In contrast, belief-level aggregation combined with simple prompting yields consistently strong disagreement-aware performance across models, while maintaining fluent and grounded summaries.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
