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.

本文提出了一种从多个政治倾向变化的新闻文章中生成中性摘要的新任务，设计了有效的度量标准和模型（NeuS-TITLE）并通过标题到文章这种层次结构的方式来实现中性化。

NeuS: 用于减轻框架偏差的中立多新闻摘要

NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

This paper explores the task of answer-aware questions generation. Based on
the attention-based pointer generator model, we propose to incorporate an
auxiliary task of language modeling to help question generation in a
hierarchical multi-task learning structure. Our joint-learning model enables
the encoder to learn a better representation of the input sequence, which will
guide the decoder to generate more coherent and fluent questions. On both SQuAD
and MARCO datasets, our multi-task learning model boosts the performance,
achieving state-of-the-art results. Moreover, human evaluation further proves
the high quality of our generated questions.

本文探讨了答案感知的问题生成任务，提出了在基于注意力机制的指针生成模型中，通过将语言建模转化为辅助任务，将问题生成纳入分层多任务学习结构。我们的联合学习模型使编码器能够学习到更好的输入序列表示，从而指导解码器生成更连贯、流畅的问题。在 SQuAD 和 MARCO 数据集上，我们的多任务学习模型提高了性能，取得了最先进的结果。此外，人类评估进一步证明了我们生成的问题的高质量。