This paper focuses on automatically generating the text of an ad, and the goal is that the generated text can capture user interest for achieving higher click-through rate (CTR). We propose CREATER, a CTR-driven advertising text generation approach, to generate ad texts based on high-quality user reviews. To incorporate CTR objective, our model learns from online A/B test data with contrastive learning, which encourages the model to generate ad texts that obtain higher CTR. To alleviate the low-resource issue, we design a customized self-supervised objective reducing the gap between pre-training and fine-tuning. Experiments on industrial datasets show that CREATER significantly outperforms current approaches. It has been deployed online in a leading advertising platform and brings uplift on core online metrics.

翻译：本文侧重于自动生成广告文本,目标是产生的文本能够捕捉用户的兴趣,实现更高的点击率(CTR)。我们建议CREETER(由CTR驱动的广告文本生成方法),在高质量的用户审查基础上生成广告文本。为了纳入CTR目标,我们的模型通过对比学习从在线A/B测试数据中学习,这鼓励模型生成获取更高CTR的广告文本。为了缓解低资源问题,我们设计了一个定制的自我监督目标,缩小培训前和微调之间的差距。工业数据集实验显示CREATER大大超越了当前的方法。它被放在一个领先的广告平台上,提高了核心在线指标。