Graph to Text

Graph-to-text generation focuses on automatically converting structured data represented as graphs into natural language text. Current research emphasizes improving the efficiency and accuracy of this process by incorporating graph structural information directly into pretrained language models (PLMs) through novel attention mechanisms or by pre-training models on unified graph representations of diverse structured data. This field is significant for advancing natural language generation in various applications, including human-robot interaction, knowledge-based question answering, and data summarization, particularly where structured knowledge is readily available.