  Machine reading comprehension is a challenging task and hot topic in natural
language processing. Its goal is to develop systems to answer the questions
regarding a given context. In this paper, we present a comprehensive survey on
different aspects of machine reading comprehension systems, including their
approaches, structures, input/outputs, and research novelties. We illustrate
the recent trends in this field based on 241 reviewed papers from 2016 to 2020.
Our investigations demonstrate that the focus of research has changed in recent
years from answer extraction to answer generation, from single to
multi-document reading comprehension, and from learning from scratch to using
pre-trained embeddings. We also discuss the popular datasets and the evaluation
metrics in this field. The paper ends with investigating the most cited papers
and their contributions.
