NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131 - 3148 July 10-15, 2022 ©2022 Association for Computational Linguistics NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias Nayeon Lee Yejin Bang Tiezheng Yu Andrea Madotto∗ Pascale Fung Hong Kong University of Science and Technology nyleeaa@connect.ust.hk, pascale@ece.ust.hk Abstract Media news framing bias can increase political polarization and undermine civil society. The need for automatic mitigation methods is there- fore growing. We propose a new task, a neu- tral summary generation from multiple news articles of the varying political leanings to fa- cilitate balanced and unbiased news reading. In this paper, we first collect a new dataset, il- lustrate 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, ar- ticle) sequentially with identifier-tokens (“TI- TLE=>”, “ARTICLE=>”) and fine-tuning the auto-regressive decoder with the standard neg- ative log-likelihood objective. We then ana- lyze and point out the remaining challenges and future directions. One of the most inter- esting observations is that neural NLG models can hallucinate not only factually inaccurate or unverifiable content but also politically biased content. 1 Introduction Media framing bias occurs when journalists make skewed decisions regarding which events or infor- mation to cover (information bias) and how to cover them (lexical bias) (Entman, 2002; Groeling, 2013). Even if the reporting of the news is based on the same set of underlying issues or facts, the fram- ing of that issue can convey a radically different impression of what actually happened (Gentzkow and Shapiro, 2006). Since the news media plays a crucial role in shaping public opinion toward vari- ous important issues (De Vreese, 2004; McCombs ∗ This work was done when the author was studying at The Hong Kong University of Science and Technology. Figure 1: Illustration of the proposed task. We want to generate neutral summarization of news articles from varying of political orientations. Orange highlights indicate phrases that can be considered framing bias. and Reynolds, 2009; Perse and Lambe, 2016), bias in media reporting can reinforce the problem of political polarization and undermining civil society rights. Allsides.com (Sides, 2018) mitigates this prob- lem by displaying articles from various media in a single interface along with an expert-written roundup of news articles. This roundup is a neu- tral summary for readers to grasp a bias-free un- derstanding of an issue before reading individual articles. Although Allsides fights framing bias, scalability still remains a bottleneck due to the time-consuming human labor needed for compos- ing the roundup. Multi-document summarization (MDS) models (Lebanoff et al., 2018; Liu and Lap- ata, 2019) could be one possible choice for automat- ing the roundup generation as both …

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