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A Survey on Machine Reading Comprehension Systems

arXiv 2001.01582 ↗First posted 6 Jan 2020cs.CLcited by 41

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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.

Key facts. Cited by 41 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Jan 2020.

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Citing papers per year: 2021: 10; 2022: 7; 2023: 9; 2024: 12; 2025: 3.

41 papers.

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  1. 2023Large Language Models Only Pass Primary School Exams in Indonesia: A Comprehensive Test on IndoMMLUcited by 53
  2. 2021ArchivalQA: A Large-scale Benchmark Dataset for Open Domain Question Answering over Historical News Collectionscited by 30
  3. 2021English Machine Reading Comprehension Datasets: A Surveycited by 26
  4. 2023Rationalization for Explainable NLP: A Surveycited by 22
  5. 2021Multi-Row, Multi-Span Distant Supervision For Table+Text Questioncited by 12
  6. 2024LLMs' Reading Comprehension Is Affected by Parametric Knowledge and Struggles with Hypothetical Statementscited by 12
  7. 2024Improving Health Question Answering with Reliable and Time-Aware Evidence Retrievalcited by 12
  8. 2024Comparing Knowledge Sources for Open-Domain Scientific Claim Verificationcited by 10
  9. 2024Temporal Blind Spots in Large Language Modelscited by 7
  10. 2024Question Difficulty Ranking for Multiple-Choice Reading Comprehensioncited by 7
  11. 2022APOLLO: A Simple Approach for Adaptive Pretraining of Language Models for Logical Reasoningcited by 5
  12. 2023MPrompt: Exploring Multi-level Prompt Tuning for Machine Reading Comprehensioncited by 5
29 more
  1. 2021ComQA:Compositional Question Answering via Hierarchical Graph Neural Networkscited by 4
  2. 2022DTW at Qur'an QA 2022: Utilising Transfer Learning with Transformers for Question Answering in a Low-resource Domaincited by 4
  3. 2022A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehensioncited by 4
  4. 2024GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answeringcited by 4
  5. 2022BioADAPT-MRC: Adversarial Learning-based Domain Adaptation Improves Biomedical Machine Reading Comprehension Taskcited by 3
  6. 2023Is it Possible to Modify Text to a Target Readability Level? An Initial Investigation Using Zero-Shot Large Language Modelscited by 3
  7. 2024Telco-DPR: A Hybrid Dataset for Evaluating Retrieval Models of 3GPP Technical Specificationscited by 3
  8. 2021Literature review on vulnerability detection using NLP technologycited by 2
  9. 2021Ensemble Learning-Based Approach for Improving Generalization Capability of Machine Reading Comprehension Systemscited by 2
  10. 2021NLP-IIS@UT at SemEval-2021 Task 4: Machine Reading Comprehension using the Long Document Transformercited by 1
  11. 2021Building a Question and Answer System for News Domaincited by 1
  12. 2021Zero-Shot Estimation of Base Models' Weights in Ensemble of Machine Reading Comprehension Systems for Robust Generalizationcited by 1
  13. 2021CogME: A Cognition-Inspired Multi-Dimensional Evaluation Metric for Story Understandingcited by 1
  14. 2022Lite Unified Modeling for Discriminative Reading Comprehensioncited by 1
  15. 2022A Comprehensive Survey on Multi-hop Machine Reading Comprehension Datasets and Metricscited by 1
  16. 2022A Comprehensive Survey on Multi-hop Machine Reading Comprehension Approachescited by 1
  17. 2023When Giant Language Brains Just Aren't Enough! Domain Pizzazz with Knowledge Sparkle Dustcited by 1
  18. 2023QASnowball: An Iterative Bootstrapping Framework for High-Quality Question-Answering Data Generationcited by 1
  19. 2023Multi-grained Evidence Inference for Multi-choice Reading Comprehensioncited by 1
  20. 2024An Information-Theoretic Approach to Analyze NLP Classification Taskscited by 1
  21. 2024VlogQA: Task, Dataset, and Baseline Models for Vietnamese Spoken-Based Machine Reading Comprehensioncited by 1
  22. 2024Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehensioncited by 1
  23. 2025FactGuard: Leveraging Multi-Agent Systems to Generate Answerable and Unanswerable Questions for Enhanced Long-Context LLM Extractioncited by 1
  24. 2025MobQA: A Benchmark Dataset for Semantic Understanding of Human Mobility Data through Question Answeringcited by 1
  25. 2023Secret-Keeping in Question Answeringcited by 0
  26. 2023KoBigBird-large: Transformation of Transformer for Korean Language Understandingcited by 0
  27. 2024Recent Advances in Multi-Choice Machine Reading Comprehension: A Survey on Methods and Datasetscited by 0
  28. 2024RoBIn: A Transformer-Based Model For Risk Of Bias Inference With Machine Reading Comprehensioncited by 0
  29. 2025Charting the Future of Scholarly Knowledge with AI: A Community Perspectivecited by 0