Framing bias plays a significant role in exacerbating political polarization by distorting the perception of actual events. Media outlets with divergent political stances often use polarized language in their reporting of the same event. We propose a new loss function that encourages the model to minimize the polarity difference between the polarized input articles to reduce framing bias. Specifically, our loss is designed to jointly optimize the model to map polarity ends bidirectionally. Our experimental results demonstrate that incorporating the proposed polarity minimization loss leads to a substantial reduction in framing bias when compared to a BART-based multi-document summarization model. Notably, we find that the effectiveness of this approach is most pronounced when the model is trained to minimize the polarity loss associated with informational framing bias (i.e., skewed selection of information to report).

翻译：框架偏见在加剧政治极化方面起着重要作用，它通过扭曲对实际事件的感知来影响公众认知。持有不同政治立场的媒体机构在报道同一事件时，常会使用极化语言。我们提出一种新型损失函数，通过鼓励模型最小化极化输入文章之间的极性差异来减少框架偏见。具体而言，该损失函数旨在联合优化模型，使其能够双向映射极性的两端。实验结果表明，与基于BART的多文档摘要模型相比，引入所提出的极性最小化损失可显著降低框架偏见。值得注意的是，我们发现当模型训练旨在最小化与信息性框架偏见（即报道内容的选择性偏差）相关的极性损失时，该方法的有效性最为显著。