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Media news framing bias can increase political polarization and underminecivil society. The need for automatic mitigation methods is therefore growing.We propose a new task, a neutral summary generation from multiple news articlesof the varying political leanings to facilitate balanced and unbiased newsreading. In this paper, we first collect a new dataset, illustrate insightsabout framing bias through a case study, and propose a new effective metric andmodel (NeuS-TITLE) for the task. Based on our discovery that title provides agood signal for framing bias, we present NeuS-TITLE that learns to neutralizenews content in hierarchical order from title to article. Our hierarchicalmulti-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 negativelog-likelihood objective. We then analyze and point out the remainingchallenges and future directions. One of the most interesting observations isthat neural NLG models can hallucinate not only factually inaccurate orunverifiable content but also politically biased content.

NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias