@inproceedings{wei-etal-2022-creater,
    title = "{CREATER}: {CTR}-driven Advertising Text Generation with Controlled Pre-Training and Contrastive Fine-Tuning",
    author = "Wei, Penghui  and
      Yang, Xuanhua  and
      Liu, ShaoGuo  and
      Wang, Liang  and
      Zheng, Bo",
    editor = "Loukina, Anastassia  and
      Gangadharaiah, Rashmi  and
      Min, Bonan",
    booktitle = "Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Industry Track",
    month = jul,
    year = "2022",
    address = "Hybrid: Seattle, Washington + Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2022.naacl-industry.2",
    doi = "10.18653/v1/2022.naacl-industry.2",
    pages = "9--17",
    abstract = "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 make use of large-scale unpaired reviews, 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.",
}