NLG System

Natural language generation (NLG) systems aim to create human-quality text from various inputs, focusing on improving both the quality and fairness of generated text. Current research emphasizes developing more efficient and robust evaluation methods, including exploring reference-free metrics and active learning techniques to reduce reliance on expensive human annotation. These advancements are crucial for building more reliable and ethically sound NLG systems, with applications ranging from automated journalism and accessibility tools to mitigating bias in news summarization and countering hate speech online.