NeuS: Neutral Multi-News Summarization for Mitigating Framing Bias

Media framing bias can lead to increased political polarization, and thus, the need for automatic mitigation methods is growing. We propose a new task, a neutral summary generation from multiple news headlines of the varying political spectrum, to facilitate balanced and unbiased news reading. In this paper, we first collect a new dataset, obtain some insights about framing bias through a case study, and propose a new effective metric and models for the task. Lastly, we conduct experimental analyses to provide insights about remaining challenges and future directions. One of the most interesting observations is that generation models can hallucinate not only factually inaccurate or unverifiable content, but also politically biased content.