让个性化新闻标题更真实，兼顾读者偏好与事实一致性
Fact-Preserved Personalized News Headline Generation
- 用历史点击相似度动态聚焦候选新闻中的关键事实
- 对比学习训练提升生成标题的事实一致性
- 适合关注个性化推荐中真实性问题的研究者
个性化新闻标题生成旨在根据读者偏好生成定制化标题，近年成为研究热点。现有方法通常通过注入用户兴趣嵌入来实现个性化，但缺乏对生成标题事实一致性的有效验证。本文提出事实保持型个性化新闻标题生成框架FPG，通过候选新闻与历史点击新闻的相似度，为关键事实分配不同注意力权重，并据此学习事实感知的全局用户嵌入。此外，设计基于对比学习的额外训练流程，进一步增强生成标题的事实一致性。在真实世界基准PENS上的大量实验验证了FPG的有效性，尤其在个性化与事实一致性之间的权衡上表现优异。
原文摘要 · Abstract (English)
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.
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