Natural Language Generation for Advertising: A Survey

Soichiro Murakami, Sho Hoshino, Peinan Zhang
6/22/2023
cs.CL

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Abstract

Natural language generation methods have emerged as effective tools to help advertisers increase the number of online advertisements they produce. This survey entails a review of the research trends on this topic over the past decade, from template-based to extractive and abstractive approaches using neural networks. Additionally, key challenges and directions revealed through the survey, including metric optimization, faithfulness, diversity, multimodality, and the development of benchmark datasets, are discussed.

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Cite this paper

@article{murakami2023natural,
  title  = {Natural Language Generation for Advertising: A Survey},
  author = {Soichiro Murakami and Sho Hoshino and Peinan Zhang},
  year   = {2023},
  eprint = {2306.12719},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.12719v1}
}

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