Evaluation of Text Generation: A Survey
Facebook AI Research（Facebook人工智能研究）；University of Washington（华盛顿大学）；Microsoft Research（微软研究院）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
