# A Survey on Machine Reading Comprehension Systems

arXiv: https://arxiv.org/abs/2001.01582 · HTML: https://mlsanity.com/paper/2001.01582/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Jan 2020.

## Built on by (most cited first, 30 of 41)

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- [Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalization](https://mlsanity.com/paper/2106.16013/) (2021)
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- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metrics](https://mlsanity.com/paper/2212.04070/) (2022)
- [A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approaches](https://mlsanity.com/paper/2212.04072/) (2022)
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## Abstract (arXiv)

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.