Updats

Dataset

Citation

@inproceedings{mita-etal-2024-striking,    title = "Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation",    author = "Mita, Masato  and      Murakami, Soichiro  and      Kato, Akihiko  and      Zhang, Peinan",    editor = "Ku, Lun-Wei  and      Martins, Andre  and      Srikumar, Vivek",    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",    month = aug,    year = "2024",    address = "Bangkok, Thailand and virtual meeting",    publisher = "Association for Computational Linguistics",    url = "https://aclanthology.org/2024.acl-long.54",    pages = "955--972",    abstract = "In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the task of ATG and propose a first benchmark dataset, CAMERA, carefully designed and enabling the utilization of multi-modal information and facilitating industry-wise evaluations. Our extensive experiments with a variety of nine baselines, from classical methods to state-of-the-art models including large language models (LLMs), show the current state and the remaining challenges. We also explore how existing metrics in ATG and an LLM-based evaluator align with human evaluations.",}

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