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Neural Question Generation from Text: A Preliminary Study

arXiv 1704.01792 ↗First posted 6 Apr 2017cs.CLcited by 78

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Abstract (arXiv)

Automatic question generation aims to generate questions from a text passage where the generated questions can be answered by certain sub-spans of the given passage. Traditional methods mainly use rigid heuristic rules to transform a sentence into related questions. In this work, we propose to apply the neural encoder-decoder model to generate meaningful and diverse questions from natural language sentences. The encoder reads the input text and the answer position, to produce an answer-aware input representation, which is fed to the decoder to generate an answer focused question. We conduct a preliminary study on neural question generation from text with the SQuAD dataset, and the experiment results show that our method can produce fluent and diverse questions.

Key facts. Cited by 78 papers in the map (data through 7 Oct 2026). First posted on arXiv 6 Apr 2017.

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Citing papers per year: 2020: 1; 2021: 20; 2022: 26; 2023: 11; 2024: 13; 2025: 7.

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  1. 2021QuestEval: Summarization Asks for Fact-based Evaluationcited by 241
  2. 2021Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answeringcited by 178
  3. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
  4. 2021Improving Question Answering Model Robustness with Synthetic Adversarial Data Generationcited by 77
  5. 2024CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Koreancited by 65
  6. 2020Text Generation by Learning from Demonstrationscited by 64
  7. 2024When Search Engine Services meet Large Language Models: Visions and Challengescited by 59
  8. 2021Zero-shot Fact Verification by Claim Generationcited by 42
  9. 2022IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languagescited by 42
  10. 2021Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewritingcited by 30
  11. 2022Learning to Answer Visual Questions from Web Videoscited by 30
  12. 2022Conditional Generation Net for Medication Recommendationcited by 28
38 more
  1. 2022A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generationcited by 28
  2. 2022Generative Language Models for Paragraph-Level Question Generationcited by 28
  3. 2022Varifocal Question Generation for Fact-checkingcited by 27
  4. 2021It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Bookscited by 23
  5. 2024Leveraging Large Language Models for Learning Complex Legal Concepts through Storytellingcited by 23
  6. 2022Question Generation for Reading Comprehension Assessment by Modeling How and What to Askcited by 20
  7. 2021ChainCQG: Flow-Aware Conversational Question Generationcited by 19
  8. 2022Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answeringcited by 19
  9. 2022RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Questioncited by 17
  10. 2024QGEval: Benchmarking Multi-dimensional Evaluation for Question Generationcited by 17
  11. 2022Shortcomings of Question Answering Based Factuality Frameworks for Error Localizationcited by 15
  12. 2023An Empirical Comparison of LM-based Question and Answer Generation Methodscited by 15
  13. 2024InfoLossQA: Characterizing and Recovering Information Loss in Text Simplificationcited by 14
  14. 2022Synthetic Question Value Estimation for Domain Adaptation of Question Answeringcited by 12
  15. 2024Automatic Database Configuration Debugging using Retrieval-Augmented Language Modelscited by 12
  16. 2023Diversify Question Generation with Retrieval-Augmented Style Transfercited by 10
  17. 2022Leaf: Multiple-Choice Question Generationcited by 9
  18. 2022QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevancecited by 9
  19. 2023Exploring the Viability of Synthetic Query Generation for Relevance Predictioncited by 9
  20. 2021Can Question Generation Debias Question Answering Models? A Case Study on Question-Context Lexical Overlapcited by 8
  21. 2021Simple or Complex? Complexity-Controllable Question Generation with Soft Templates and Deep Mixture of Experts Modelcited by 8
  22. 2023Evaluation of Question Generation Needs More Referencescited by 8
  23. 2021Improving Non-autoregressive Generation with Mixup Trainingcited by 7
  24. 2025Savaal: Scalable Concept-Driven Question Generation to Enhance Human Learningcited by 7
  25. 2022Downstream Transformer Generation of Question-Answer Pairs with Preprocessing and Postprocessing Pipelinescited by 5
  26. 2022Evaluation of Question Answering Systems: Complexity of judging a natural languagecited by 5
  27. 2023Automatic Answerability Evaluation for Question Generationcited by 5
  28. 2023Diversity Enhanced Narrative Question Generation for Storybookscited by 5
  29. 2024Automatic Generation and Evaluation of Reading Comprehension Test Items with Large Language Modelscited by 5
  30. 2022Towards Zero-Shot and Few-Shot Table Question Answering using GPT-3cited by 4
  31. 2022In-sample Curriculum Learning by Sequence Completion for Natural Language Generationcited by 4
  32. 2021Quinductor: a multilingual data-driven method for generating reading-comprehension questions using Universal Dependenciescited by 3
  33. 2021Generating Answer Candidates for Quizzes and Answer-Aware Question Generatorscited by 3
  34. 2021Automated question generation and question answering from Turkish textscited by 3
  35. 2021Improving Controllability of Educational Question Generation by Keyword Provisioncited by 3
  36. 2024Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trendscited by 3
  37. 2024Graph Guided Question Answer Generation for Procedural Question-Answeringcited by 3
  38. 2024E-QGen: Educational Lecture Abstract-based Question Generation Systemcited by 3

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