  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.
