  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.
