Evaluation of Text Generation: A Survey
It provides a comprehensive overview for researchers and practitioners in NLG, but is incremental as it synthesizes existing work without new results.
The paper surveys evaluation methods for natural language generation (NLG) systems, categorizing them into human-centric, automatic, and machine-learned metrics, and discusses progress, challenges, and future directions.
The paper surveys evaluation methods of natural language generation (NLG) systems that have been developed in the last few years. We group NLG evaluation methods into three categories: (1) human-centric evaluation metrics, (2) automatic metrics that require no training, and (3) machine-learned metrics. For each category, we discuss the progress that has been made and the challenges still being faced, with a focus on the evaluation of recently proposed NLG tasks and neural NLG models. We then present two examples for task-specific NLG evaluations for automatic text summarization and long text generation, and conclude the paper by proposing future research directions.