COBART：用于广告标题生成的可控、优化、双向和自回归变换器
COBART: Controlled, Optimized, Bidirectional and Auto-Regressive Transformer for Ad Headline Generation
AI总结：
研究针对广告标题生成难题，提出用前缀控制令牌结合BART微调的方法，可控制标题长度，适应不同格式与要求，实验显示该方法能提升Rouge-L和估计CTR，相比基线有显著提高。
摘要：
在线广告对所有企业都至关重要，广告标题是其核心创意组成部分之一。现有方法能自动生成标题并优化点击率（CTR）和质量，但不断演变的广告格式和创意要求使生成优化定制标题变得困难。我们提出一种使用前缀控制令牌和BART微调的新方法，它能产生最高CTR，还允许用户控制生成标题的长度以用于不同广告格式。该方法灵活，可轻松适应其他架构、创意要求和优化标准。实验表明，与之前发表的强大广告标题生成基线相比，Rouge-L提高了25.82%，估计CTR提高了5.82%。
英文摘要：
Online ads are essential to all businesses and ad headlines are one of their core creative component. Existing methods can generate headlines automatically and also optimize their click-through-rate (CTR) and quality. However, evolving ad formats and changing creative requirements make it difficult to generate optimized & customized headlines. We propose a novel method that uses prefix control tokens along with BART fine-tuning. It yields the highest CTR and also allows users to control the length of generated headlines for use across different ad formats. The method is also flexible and can easily be adapted to other architectures, creative requirements and optimization criteria. Our experiments demonstrate a 25.82% increment in Rouge-L and a 5.82% increment in estimated CTR over previously published strong ad headline generation baseline.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
