Data to Text Generation

Data-to-text generation (D2T) focuses on automatically creating human-readable text from structured data sources like tables and graphs. Current research emphasizes improving the accuracy, fluency, and faithfulness of generated text, often using large language models (LLMs) fine-tuned on diverse datasets and employing techniques like contrastive learning and cycle training to enhance model performance. This field is significant because it enables automated report generation, knowledge base summarization, and improved accessibility to information across various domains and languages, particularly benefiting low-resource settings.