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

What Constitutes a Faithful Summary? Preserving Author Perspectives in  News Summarization

Identifying political perspectives in news media has become an important task
due to the rapid growth of political commentary and the increasingly polarized
political ideologies. Previous approaches focus on textual content and leave
out the rich social and political context that is essential in the perspective
detection process. To address this limitation, we propose KGAP, a political
perspective detection method that incorporates external domain knowledge.
Specifically, we construct a political knowledge graph to serve as
domain-specific external knowledge. We then construct heterogeneous information
networks to represent news documents, which jointly model news text and
external knowledge. Finally, we adopt relational graph neural networks and
conduct political perspective detection as graph-level classification.
Extensive experiments demonstrate that our method consistently achieves the
best performance on two real-world perspective detection benchmarks. Ablation
studies further bear out the necessity of external knowledge and the
effectiveness of our graph-based approach.

本文提出了一种利用外部领域知识进行政治观点识别的 KGAP 方法，该方法在构造政治知识图谱的基础上，结合异构信息网络来表示新闻文本与外部知识，并采用关系图神经网络进行政治观点检测，实验结果表明该方法在现实世界的识别标准中表现最佳。