Current methods for generating attractive headlines often learn directly from data, which bases attractiveness on the number of user clicks and views. Although clicks or views do reflect user interest, they can fail to reveal how much interest is raised by the writing style and how much is due to the event or topic itself. Also, such approaches can lead to harmful inventions by over-exaggerating the content, aggravating the spread of false information. In this work, we propose HonestBait, a novel framework for solving these issues from another aspect: generating headlines using forward references (FRs), a writing technique often used for clickbait. A self-verification process is included during training to avoid spurious inventions. We begin with a preliminary user study to understand how FRs affect user interest, after which we present PANCO1, an innovative dataset containing pairs of fake news with verified news for attractive but faithful news headline generation. Automatic metrics and human evaluations show that our framework yields more attractive results (+11.25% compared to human-written verified news headlines) while maintaining high veracity, which helps promote real information to fight against fake news.

本研究提出 HonestBait 框架，使用前向引用技术生成标语来避免过度夸张的内容和传播虚假信息的风险，并进行自我验证过程来避免虚假发明，并通过人工和自动的评估得出，该框架生成的标语比人工编写的验证新闻标语更有吸引力，同时保持高保真度，有助于推广真实信息以对抗虚假信息。

HonestBait：用于生成吸引人但忠实的标题的前向引用技术