Mean Average Precision (MAP) is an important evaluation metric for keyword extraction tasks in natural language processing. MAP averages precision at different recall positions across all queries, providing a comprehensive measure of the overall quality of keyword ranking. It is more comprehensive than single-point metrics and can reflect the model's sustained performance across different recall levels. It is usually reported as a percentage or a 0–1 score. This metric is widely used in relevant benchmarks and papers and can effectively reflect the model's performance on this dimension. Higher values are better.