Data-to-Text Generation

Data-to-Text Generation is a classic problem in the field of natural language processing, aiming to convert structured data into fluent and accurate natural language text. This task not only involves selecting appropriate content from the input data for description but also requires the use of surface realization techniques to generate natural and coherent expressions to meet the needs of different application scenarios, such as automatic report generation, weather forecasts, and news summaries.