In this work, we take a first step towards designing summarization systems that are faithful to the author's opinions and perspectives. Focusing on a case study of preserving political perspectives in news summarization, we find that existing approaches alter the political opinions and stances of news articles in more than 50% of summaries, misrepresenting the intent and perspectives of the news authors. We thus propose P^3Sum, a diffusion model-based summarization approach controlled by political perspective classifiers. In P^3Sum, the political leaning of a generated summary is iteratively evaluated at each decoding step, and any drift from the article's original stance incurs a loss back-propagated to the embedding layers, steering the political stance of the summary at inference time. Extensive experiments on three news summarization datasets demonstrate that P^3Sum outperforms state-of-the-art summarization systems and large language models by up to 11.4% in terms of the success rate of stance preservation, with on-par performance on standard summarization utility metrics. These findings highlight the lacunae that even for state-of-the-art models it is still challenging to preserve author perspectives in news summarization, while P^3Sum presents an important first step towards evaluating and developing summarization systems that are faithful to author intent and perspectives.

本文介绍了一种基于政治观点分类器的传播模型的摘要方法，名为P^3Sum。通过在解码步骤中迭代评估生成摘要的政治倾向，并在推理时对偏移进行损失反向传播给嵌入层，P^3Sum方法在新闻摘要中成功保持立场的百分比方面优于现有技术，并在标准摘要实用性度量方面表现相当。这些发现凸显了即使对于最先进的模型也在新闻摘要中保持作者观点的困难，而P^3Sum则是评估和开发忠实于作者意图和观点的摘要系统的重要第一步。

如何构成一份忠实的摘要？在新闻摘要中保留作者的观点