@inproceedings{inoue-etal-2024-camera3,
    title = "{CAMERA}{\mbox{$^3$}}: An Evaluation Dataset for Controllable Ad Text Generation in {J}apanese",
    author = "Inoue, Go  and
      Kato, Akihiko  and
      Mita, Masato  and
      Honda, Ukyo  and
      Zhang, Peinan",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.242",
    pages = "2702--2707",
    abstract = "Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called {``}ad fatigue,{''} where users become disengaged due to repetitive exposure to the same advertisement. Despite numerous efforts in ad text generation, the aspect of diversifying ad texts has received limited attention, particularly in non-English languages like Japanese. To address this, we present CAMERA{\mbox{$^3$}}, an evaluation dataset for controllable text generation in the advertising domain in Japanese. Our dataset includes 3,980 ad texts written by expert annotators, taking into account various aspects of ad appeals. We make CAMERA{\mbox{$^3$}} publicly available, allowing researchers to examine the capabilities of recent NLG models in controllable text generation in a real-world scenario.",
}

Ad text generation is the task of creating compelling text from an advertising asset that describes products or services, such as a landing page. In advertising, diversity plays an important role in enhancing the effectiveness of an ad text, mitigating a phenomenon called {``}ad fatigue,{''} where users become disengaged due to repetitive exposure to the same advertisement. Despite numerous efforts in ad text generation, the aspect of diversifying ad texts has received limited attention, particularly in non-English languages like Japanese. To address this, we present CAMERA{\mbox{$^3$}}, an evaluation dataset for controllable text generation in the advertising domain in Japanese. Our dataset includes 3,980 ad texts written by expert annotators, taking into account various aspects of ad appeals. We make CAMERA{\mbox{$^3$}} publicly available, allowing researchers to examine the capabilities of recent NLG models in controllable text generation in a real-world scenario.
Inoue, Go Kato, Akihiko Mita, Masato Honda, Ukyo Zhang, Peinan
CAMERA: An Evaluation Dataset for Controllable Ad Text Generation in Japanese