CAMERA: A Multimodal Dataset and Benchmark for Ad Text Generation
Masato Mita Soichiro Murakami Akihiko Kato Peinan Zhang
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.