Natural Language Generation for Advertising: A Survey
Abstract
Research trends in natural language generation for online advertising are reviewed, covering template-based, extractive, and abstractive approaches, and discussing challenges like metric optimization, faithfulness, diversity, multimodality, and benchmark datasets.
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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