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).

通过减少极化性输入文章间的极性差异，来降低造成政治极化的框架偏见，我们提出了一种新的损失函数，使模型能够在两个极性极端之间进行双向映射优化。实验证明，结合提出的极性损失函数的效果远优于基于BART的多文档摘要模型，尤其是在训练模型以最小化信息框架偏见（即选择性报道）的极性损失时。

使用极性最小化损失缓解构架偏倚