NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, Pascale Fung


Abstract
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 leaningsto 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.
Anthology ID:
2022.naacl-main.228
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3131–3148
Language:
URL:
https://aclanthology.org/2022.naacl-main.228
DOI:
10.18653/v1/2022.naacl-main.228
Bibkey:
Cite (ACL):
Nayeon Lee, Yejin Bang, Tiezheng Yu, Andrea Madotto, and Pascale Fung. 2022. NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3131–3148, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias (Lee et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.228.pdf
Code
 hltchkust/framing-bias-metric
Data
Multi-News