CAMERA: A Multimodal Dataset and Benchmark for Ad Text Generation

Masato Mita, Soichiro Murakami, Akihiko Kato, Peinan Zhang

Abstract: In response to the limitations of manual online ad production, significant research has been conducted in the field of automatic ad text generation (ATG). However, comparing different methods has been challenging because of the lack of benchmarks encompassing the entire field and the absence of well-defined problem sets with clear model inputs and outputs. To address these challenges, this paper aims to advance the field of ATG by introducing a redesigned task and constructing a benchmark. Specifically, we defined ATG as a cross-application task encompassing various aspects of the Internet advertising. As part of our contribution, we propose a first benchmark dataset, CA Multimodal Evaluation for Ad Text GeneRAtion (CAMERA), carefully designed for ATG to be able to leverage multi-modal information and conduct an industry-wise evaluation. Furthermore, we demonstrate the usefulness of our proposed benchmark through evaluation experiments using multiple baseline models, which vary in terms of the type of pre-trained language model used and the incorporation of multi-modal information. We also discuss the current state of the task and the future challenges.