Global partisan hostility and polarization has increased, and this polarization is heightened around presidential elections. Models capable of generating accurate summaries of diverse perspectives can help reduce such polarization by exposing users to alternative perspectives. In this work, we introduce a novel dataset and task for independently summarizing each political perspective in a set of passages from opinionated news articles. For this task, we propose a framework for evaluating different dimensions of perspective summary performance. We benchmark 10 models of varying sizes and architectures through both automatic and human evaluation. While recent models like GPT-4o perform well on this task, we find that all models struggle to generate summaries faithful to the intended perspective. Our analysis of summaries focuses on how extraction behavior depends on the features of the input documents.

本研究解决了在全球政党对立和两极化愈加严重的背景下，如何有效摘要不同政治观点的难题。提出了一种新颖的数据集和任务，通过评估不同模型的表现，发现尽管近期的模型如GPT-4o表现良好，仍然难以生成忠实于特定视角的摘要。研究结果显示，摘要的提取行为与输入文档的特征密切相关，具有重要的应用潜力。 

多元视角下的政治文件摘要研究