Natural Language Generation for Advertising: A Survey
Soichiro Murakami Sho Hoshino Peinan Zhang
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.