Personalized news headline generation, aiming at generating user-specific headlines based on readers' preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoderdecoder headline generator to make the output personalized, while the factual consistency of headlines is inadequate to be verified. In this paper, we propose a framework Fact-Preserved Personalized News Headline Generation (short for FPG), to prompt a tradeoff between personalization and consistency. In FPG, the similarity between the candidate news to be exposed and the historical clicked news is used to give different levels of attention to key facts in the candidate news, and the similarity scores help to learn a fact-aware global user embedding. Besides, an additional training procedure based on contrastive learning is devised to further enhance the factual consistency of generated headlines. Extensive experiments conducted on a real-world benchmark PENS validate the superiority of FPG, especially on the tradeoff between personalization and factual consistency.

翻译：个性化新闻标题生成旨在根据读者偏好生成用户特定的标题，已成为近期蓬勃发展的研究方向。现有研究通常将用户兴趣嵌入注入编码器-解码器标题生成器以实现个性化输出，但标题的事实一致性尚未得到充分验证。本文提出事实保持的个性化新闻标题生成框架（简称FPG），以促进个性化与一致性的权衡。在FPG中，通过待曝光候选新闻与历史点击新闻的相似度，对候选新闻中的关键事实给予不同级别的关注，相似度分数有助于学习事实感知的全局用户嵌入。此外，设计了基于对比学习的额外训练流程，以进一步增强生成标题的事实一致性。在真实基准数据集PENS上进行的大量实验验证了FPG的优越性，特别是在个性化与事实一致性的权衡方面。