Media news framing bias can increase political polarization and undermine civil society. The need for automatic mitigation methods is therefore growing. We propose a new task, a neutral summary generation from multiple news articles of the varying political leanings to facilitate balanced and unbiased news reading. In this paper, we first collect a new dataset, illustrate insights about framing bias through a case study, and propose a new effective metric and model (NeuS-TITLE) for the task. Based on our discovery that title provides a good signal for framing bias, we present NeuS-TITLE that learns to neutralize news content in hierarchical order from title to article. Our hierarchical multi-task learning is achieved by formatting our hierarchical data pair (title, article) sequentially with identifier-tokens ("TITLE=>", "ARTICLE=>") and fine-tuning the auto-regressive decoder with the standard negative log-likelihood objective. We then analyze and point out the remaining challenges and future directions. One of the most interesting observations is that neural NLG models can hallucinate not only factually inaccurate or unverifiable content but also politically biased content.
翻译:设置媒体偏见的媒体新闻可能会加剧政治两极分化,破坏民间社会。 因此,自动缓解方法的需要正在增加。 我们提出一个新的任务,即从不同政治倾斜的多条新闻文章中产生中性摘要,以促进平衡和不偏不倚的新闻阅读。 在本文中,我们首先收集新的数据集,通过案例研究说明偏见的洞察力,并为这项任务提出新的有效指标和模型(Neus-TITLE) 。根据我们发现的标题为构建偏见提供了良好的信号,我们介绍了Neus-TITLE, 从标题到文章,学会从等级顺序上消除新闻内容。我们分级的多任务学习是通过编成我们的等级数据配对(标题、文章)实现的。我们的等级性多任务学习是按顺序以识别符号(“TITLEQ ” 、 “ARTICLEQ ” ) 和微调自动反偏重的分解器与标准负逻辑相似的目标一起实现的。 我们然后分析和指出其余的挑战和未来的方向。 我们最有趣的观察是, 神经 NLG 模型不仅可以想象出事实上或无法令人信服的内容, 。