Table To Text Generation

Table-to-Text Generation is a subtask in the field of Natural Language Processing, aimed at converting structured data tables into coherent natural language text. The goal of this task is to generate accurate, fluent, and information-rich descriptive texts by analyzing the relationships and patterns within the table data. Its application value lies in the ability to automate the transformation of complex data into an easily understandable textual format, enhancing the efficiency of data interpretation and dissemination. It is applicable to various scenarios such as news reporting, data analysis reports, and automatic summarization.