  Machine Reading Comprehension (MRC) is a challenging Natural Language
Processing(NLP) research field with wide real-world applications. The great
progress of this field in recent years is mainly due to the emergence of
large-scale datasets and deep learning. At present, a lot of MRC models have
already surpassed human performance on various benchmark datasets despite the
obvious giant gap between existing MRC models and genuine human-level reading
comprehension. This shows the need for improving existing datasets, evaluation
metrics, and models to move current MRC models toward "real" understanding. To
address the current lack of comprehensive survey of existing MRC tasks,
evaluation metrics, and datasets, herein, (1) we analyze 57 MRC tasks and
datasets and propose a more precise classification method of MRC tasks with 4
different attributes; (2) we summarized 9 evaluation metrics of MRC tasks, 7
attributes and 10 characteristics of MRC datasets; (3) We also discuss key open
issues in MRC research and highlighted future research directions. In addition,
we have collected, organized, and published our data on the companion
website(https://mrc-datasets.github.io/) where MRC researchers could directly
access each MRC dataset, papers, baseline projects, and the leaderboard.
