Concept To Text Generation

Concept-to-Text Generation is a significant sub-task in the field of Natural Language Processing, aimed at automatically generating natural language text from conceptual representations (such as ontologies). The goal of this task is to convert structured or semi-structured concept information into fluent, coherent, and semantically accurate text to achieve effective communication and understanding of information. Its application value is extensive, including but not limited to the textualization of knowledge graphs, answer generation for intelligent Q&A systems, automatic summarization, and report writing, all of which can significantly enhance the efficiency and quality of information processing.