Simplifying Paragraph-Level Question Generation via Transformer Language Models

Attention Is All You Need

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Adam: A Method for Stochastic Optimization

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The Curious Case of Neural Text Degeneration

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Unified Language Model Pre-training for Natural Language Understanding and Generation

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Learning to Ask: Neural Question Generation for Reading Comprehension

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Neural Question Generation from Text: A Preliminary Study

Zhou, Q. S., Yang, N., Wei, F., Tan, C., Bao, H., & Zhou, M. (2018) · Lecture notes in computer science · doi:10.1007/978-3-319-73618-1_56 · Cited by 353 · see citing works

Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks

Zhao, Y., Ni, X., Ding, Y., & Ke, Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1424 · Cited by 288 · see citing works

Question Generation for Question Answering

Duan, N., Tang, D., Chen, P., & Zhou, M. (2017) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d17-1090 · Cited by 271 · see citing works

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language GenerationPreprint

Sharma, S., Asri, L. E., Schulz, H., & Zumer, J. (2017) · arXiv.org · doi:10.48550/arxiv.1706.09799 · Cited by 250 · see citing works

Harvesting Paragraph-level Question-Answer Pairs from Wikipedia

Du, X., & Cardie, C. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1177 · Cited by 168 · see citing works

Machine Comprehension by Text-to-Text Neural Question Generation

Yuan, X., Wang, T., Gülçehre, Ç., Sordoni, A., Bachman, P., Zhang, S., Subramanian, S., & Trischler, A. (2017) · Workshop on Representation Learning for NLP (RepL4NLP) · doi:10.18653/v1/w17-2603 · Cited by 158 · see citing works

Assessing Factoid Question-Answer Generation for Portuguese (Short Paper)Preprint

Yuan, X., Wang, T., Gülçehre, Ç., et al. (2017) · arXiv (Cornell University) · doi:10.4230/oasics.slate.2020.16 · Cited by 50 · see citing works

Machine Comprehension by Text-to-Text Neural Question GenerationPreprint

Yuan, X., Wang, T., Gülçehre, Ç., Sordoni, A., Bachman, P., Subramanian, S., Zhang, S., & Trischler, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1705.02012 · Cited by 40 · see citing works

ScanQA: 3D Question Answering for Spatial Scene Understanding

Azuma, D., Miyanishi, T., Kurita, S., & Kawanabe, M. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01854 · Cited by 197 · see citing works

Automatic question generation: a review of methodologies, datasets, evaluation metrics, and applications

Mulla, N., & Gharpure, P. (2023) · Progress in Artificial Intelligence · doi:10.1007/s13748-023-00295-9 · Cited by 132 · see citing works

AI-based learning content generation and learning pathway augmentation to increase learner engagement

Diwan, C., Srinivasa, S., Suri, G., Agarwal, S., & Ram, P. (2022) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2022.100110 · Cited by 122 · see citing works

Scalable Educational Question Generation with Pre-trained Language Models

Bulathwela, S., Muse, H., & Yılmaz, E. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-36272-9_27 · Cited by 71 · see citing works

Automated Question and Answer Generation from Texts using Text-to-Text Transformers

Goyal, R., Kumar, P., & Singh, V. P. (2023) · Arabian Journal for Science and Engineering · doi:10.1007/s13369-023-07840-7 · Cited by 46 · see citing works

Student-AI Question Cocreation for Enhancing Reading Comprehension

Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2023) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2023.3333439 · Cited by 34 · see citing works

Review on Neural Question Generation for Education Purposes

Faraby, S. A., Adiwijaya, A., & Romadhony, A. (2023) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00374-x · Cited by 31 · see citing works

Ensemble-NQG-T5: Ensemble Neural Question Generation Model Based on Text-to-Text Transfer Transformer

Hwang, M.-H., Shin, J., Seo, H., Im, J.-S., Cho, H., & Lee, C.-K. (2023) · Applied Sciences · doi:10.3390/app13020903 · Cited by 25 · see citing works

Formative Feedback on Student-Authored Summaries in Intelligent Textbooks Using Large Language Models

Morris, W., Crossley, S. A., Holmes, L., Ou, C., Dascălu, M., & McNamara, D. S. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-024-00395-0 · Cited by 20 · see citing works

Survey of Natural Language Processing for Education: Taxonomy, Systematic Review, and Future Trends

Lan, Y., Li, X., Du, H., Lu, X., Gao, M. Z., Qian, W., & Zhou, A. (2025) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2025.3621181 · Cited by 43 · see citing works

Automated multiple-choice question generation in Spanish using neural language models

de-Fitero-Dominguez, D., García‐Cabot, A., & García‐López, E. (2024) · Neural Computing and Applications · doi:10.1007/s00521-024-10076-7 · Cited by 14 · see citing works

NER2QUES: combining named entity recognition and sequence to sequence to automatically generating Vietnamese questions

Phan, T. H. V., & Do, P. (2021) · Neural Computing and Applications · doi:10.1007/s00521-021-06477-7 · Cited by 13 · see citing works

Automated Generation and Evaluation of MultipleChoice Quizzes using Langchain and Gemini LLM

Pawar, P., Dube, R., Joshi, A., Gulhane, Z., & Patil, R. (2024) · International Conference on Electrical Electronics and Computing Technologies (ICEECT) · doi:10.1109/iceect61758.2024.10739326 · Cited by 12 · see citing works

A Novel Approach to Scalable and Automatic Topic-Controlled Question Generation in Education

Li, Z., Cukurova, M., & Bulathwela, S. (2025) · International Conference on Learning Analytics and Knowledge (LAK) · doi:10.1145/3706468.3706487 · Cited by 12 · see citing works

Evaluation of Question Answering Systems: Complexity of Judging a Natural Language

Farea, A., Yang, Z., Duong, K., Perera, N., & Emmert‐Streib, F. (2025) · ACM Computing Surveys · doi:10.1145/3744663 · Cited by 21 · see citing works

Automatic Question Generation Using Natural Language Processing and Transformers

Elshiny, R. M., & Hamdy, A. (2023) · International Conference on Computer and Applications (ICCA) · doi:10.1109/icca59364.2023.10401848 · Cited by 9 · see citing works

Generation of Multiple Choice Questions from Indian Educational Text

Saddish, A., Somaiah, P., Gambhir, V., & Nirmala, S. J. (2023) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon58793.2023.10270551 · Cited by 8 · see citing works

PRICAI 2021: Trends in Artificial Intelligence

(2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89363-7 · Cited by 6 · see citing works

Leveraging Structured Information from a Passage to Generate Questions

Xu, J., Sun, Y., Gan, J., Zhou, M., & Wu, D. (2022) · Tsinghua Science & Technology · doi:10.26599/tst.2022.9010034 · Cited by 6 · see citing works

Towards Human-Like Educational Question Generation with Small Language Models

Fawzi, F., Balan, S., Cukurova, M., Yılmaz, E., & Bulathwela, S. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-64315-6_25 · Cited by 5 · see citing works

Enhancing Vietnamese Question Generation with Reinforcement Learning

Nguyen, V., & Nguyen, K. V. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21743-2_45 · Cited by 5 · see citing works

QASem Parsing: Text-to-text Modeling of QA-based Semantics

Klein, A., Hirsch, E., Eliav, R., Pyatkin, V., Caciularu, A., & Dagan, I. (2022) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2022.emnlp-main.528 · Cited by 4 · see citing works

Advancing Bangla NLP: Transformer-Based Question Generation using Fine-Tuned LLM

Faieaz, W. W., Jannat, S., Mondal, P. K., Khan, S. S., Karmaker, S., & Rahman, M. S. (2025) · International Conference on Electrical, Computer and Communication Engineering · doi:10.1109/ecce64574.2025.11013206 · Cited by 4 · see citing works

AUTOMATIC QUESTION GENERATION THROUGH WORD VECTOR SYNCHRONIZATION USING LAMMA

Khandait, K., Bhura, S. A., & Asole, S. S. (2022) · Indian Journal of Computer Science and Engineering · doi:10.21817/indjcse/2022/v13i4/221304046 · Cited by 4 · see citing works

An end-to-end transformer-based model for Arabic question generation

Lafkiar, S., & En-Nahnahi, N. (2024) · Multimedia Tools and Applications · doi:10.1007/s11042-024-19958-3 · Cited by 3 · see citing works

Question Generation Capabilities of “Small" Large Language Models

Berger, J., Koß, J., Stamatakis, M., Hoppe, A., Ewerth, R., & Wartena, C. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-70242-6_18 · Cited by 4 · see citing works

“Questomatic”: Automated Question Formulation System

Jadhav, P., Deore, S., Aute, S., Gaikwad, R., Barphe, S. S., & Meshram, E. (2024) · International Conference on Sustainable Expert Systems (ICSES) · doi:10.1109/icses63445.2024.10763124 · Cited by 3 · see citing works

Investigating legal question generation using large language models

Deroy, A., Ghosh, K., & Ghosh, S. (2025) · Artificial Intelligence and Law · doi:10.1007/s10506-025-09452-y · Cited by 5 · see citing works

Descriptor: Open-Domain Long-Form Context-Aware Question-Answering Dataset (DragonVerseQA)

Lahiri, A. K., & Hu, Q. (2025) · IEEE data descriptions. · doi:10.1109/ieeedata.2025.3562173 · Cited by 3 · see citing works

Refined-Para Forming Question Generation System Using Lamma

Khandait, K., Bhura, S. A., & Asole, S. S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-24352-3_6 · Cited by 2 · see citing works

QGen: Automated Question Paper Generator

Paul, R. J., Jamal, S., Bejoy, S., Daniel, R. J., & Aju, N. (2024) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit61487.2024.10580391 · Cited by 2 · see citing works

PRICAI 2021: Trends in Artificial Intelligence

(2021) · Lecture notes in computer science · doi:10.1007/978-3-030-89188-6 · Cited by 2 · see citing works

Enhancing Question Generation in Bahasa Using Pretrained Language Models

Fredyan, R., Wiyono, I. S., Suhartono, D., Majiid, M. R. N., & Purnomo, F. (2024) · Revue d intelligence artificielle · doi:10.18280/ria.380421 · Cited by 1 · see citing works

Constructing a Closed-Domain Question Answering System with Generative Language Models

Le, H., Nguyen, L.-M., Ni, J., & Okada, S. (2023) · International Conference on Knowledge and Systems Engineering/Proceedings · doi:10.1109/kse59128.2023.10299437

Improving Vietnamese Question-Answering system with Data Augmentation and Optimization

Bac, N. B. T., & Vinh, N. V. (2023) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf60135.2023.10471797

Modern Approaches for Automatic Question Paper Generator

Ramar, G., Ramkumar, S., Stephen, R., Balan, R. V. S., & Sahu, M. (2025) · International Conference on Computing Technologies & Data Communication (ICCTDC) · doi:10.1109/icctdc64446.2025.11158798

Towards automatic understanding of narrative audiovisual content

Reboud, A. (2022) · theses.fr (ABES) · doi:10.70675/7b1296fez9f91z4484z8448z8040d28ab833

Knowledge Modeling and Multilingual Information Extraction for the Understanding of the Cultural Heritage of Silk

Schleider, T. (2022) · theses.fr (ABES) · doi:10.70675/6f2b45eez5308z4992z9e4azd2a0ac72f19d

Generating and answering questions across text and knowledge graphs

Han, K. (2024) · theses.fr (ABES) · doi:10.70675/605774e1z4ab3z4bc8z9628ze56d226162f9

Towards a Realistic Approach for an Automatic Question Generation System

Singh, M., Patvardhan, C., & Lakshmi, C. V. (2026) · International Conference on Sustainable Engineering and Technology Innovations (ICSETI) · doi:10.1109/icseti67678.2026.11637059

Unlocking deeper understanding: A type-specific contextualized question generation approach

Dey, D., Rudrapal, D., Jamatia, A., & Chakma, K. (2026) · Sadhana · doi:10.1007/s12046-026-03398-w

Transformer-based End-to-End Question Generation.Preprint

Lopez, L. E., Cruz, D. K., Cruz, J. C. B., & Cheng, C. (2020) · arXiv (Cornell University) · Cited by 27 · see citing works

Improving paragraph-level question generation with extended answer network and uncertainty-aware beam search

Zeng, H., Zhi, Z., Liu, J., & Wei, B. (2021) · Information Sciences · doi:10.1016/j.ins.2021.04.026 · Cited by 14 · see citing works

Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks

Zhao, Y., Ni, X., Ding, Y., & Ke, Q. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1424 · Cited by 288 · see citing works

Transformer-Based Multi-Hop Question Generation (Student Abstract)

Emerson, J., & Chali, Y. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i13.26963 · Cited by 3 · see citing works

Keyword Aware Question Generation Based on Pre-Trained Language Model

YU Zunrui, M. Z. (2022) · DOAJ (DOAJ: Directory of Open Access Journals) · doi:10.19678/j.issn.1000-3428.0060501