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.

翻译：观点摘要与多文档摘要常涉及真实存在的观点冲突，然而现有方法（尤其是基于大语言模型的系统）往往隐式地消解异议并过度呈现主流观点，这在观点密集的场景中限制了生成摘要的忠实性。本文提出一种异议感知的合成流程，将信念层级的聚合与语言生成相分离。首先将文档表示为结构化信念集合，并采用基于距离的信念融合算子进行显式冲突建模的聚合操作，随后仅使用大语言模型将聚合后的信念实现为自然语言摘要。我们在多种模型系列与规模上评估该方法，并与在生成过程中执行显式聚合的技术进行对比。实验结果表明：当聚合操作在生成阶段处理时，足够大规模的模型虽能达到信念层级聚合的效果，但该行为在不同架构或能力模型中并不稳定。相比之下，信念层级聚合结合简易提示策略能在各类模型中持续产生优异的异议感知性能，同时保持摘要的流畅性与事实依据。