Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2023
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913393043046400 |
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| author | Mita, Masato Murakami, Soichiro Kato, Akihiko Zhang, Peinan |
| author_facet | Mita, Masato Murakami, Soichiro Kato, Akihiko Zhang, Peinan |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_12030 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation Mita, Masato Murakami, Soichiro Kato, Akihiko Zhang, Peinan Computation and Language 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. |
| title | Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2309.12030 |