Persuasive Natural Language Generation -- A Literature Review
It provides a structured overview for researchers and practitioners in business and NLG, but is incremental as it synthesizes existing knowledge without new empirical results.
This literature review examines how Natural Language Generation (NLG) can be used to automatically detect and generate persuasive texts, focusing on five business-related categories to enhance message persuasiveness, based on an analysis of 77 articles.
This literature review focuses on the use of Natural Language Generation (NLG) to automatically detect and generate persuasive texts. Extending previous research on automatic identification of persuasion in text, we concentrate on generative aspects through conceptualizing determinants of persuasion in five business-focused categories: benevolence, linguistic appropriacy, logical argumentation, trustworthiness, tools and datasets. These allow NLG to increase an existing message's persuasiveness. Previous research illustrates key aspects in each of the above mentioned five categories. A research agenda to further study persuasive NLG is developed. The review includes analysis of seventy-seven articles, outlining the existing body of knowledge and showing the steady progress in this research field.