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 124 reviewed papers from 2016 to 2018. 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.

本文对2016年到2020年间关于机器阅读理解方面的241篇论文进行了全面的调查，探讨了机器阅读理解的不同方面，包括方法、结构、输入/输出和研究的创新之处，并论述了研究的重点发生了变化，从答案提取到答案生成，从单一到多文档阅读理解，从零开始学习到使用预训练的嵌入。同时我们还讨论了这个领域中的流行数据集和评估指标，最后还研究了最常被引用的论文及其贡献。

机器阅读理解系统调查