Content Planning

Content planning in natural language generation aims to improve text coherence and quality by strategically organizing information before generating the final output. Current research focuses on integrating content planning into various generation tasks, such as summarization, question answering, and recipe generation, often employing transformer-based models enhanced with mechanisms for selecting key phrases, generating intermediate representations (like content plans or EDUs), and leveraging these plans to guide the generation process. These advancements demonstrate improved performance in automatic metrics and, in some cases, human evaluation, highlighting the importance of content planning for generating more informative, coherent, and relevant text across diverse applications.