A Systematic Review of Data-to-Text NLG

This systematic review aims to provide a comprehensive analysis of the state of data-to-text generation research, focusing on identifying research gaps, offering future directions, and addressing challenges found during the review. We thoroughly examined the literature, including approaches, datasets, evaluation metrics, applications, multilingualism, and hallucination mitigation measures. Our review provides a roadmap for future research in this rapidly evolving field.

analysis applications challenges cs.ai cs.cl cs.lg data datasets evaluation evaluation metrics found future hallucination literature metrics multilingualism nlg research review roadmap state text text generation