# Neural Question Generation from Text: A Preliminary Study

arXiv: https://arxiv.org/abs/1704.01792 · HTML: https://mlsanity.com/paper/1704.01792/

## Key facts

- Cited by 78 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Apr 2017.

## Built on by (most cited first, 30 of 78)

- [QuestEval: Summarization Asks for Fact-based Evaluation](https://mlsanity.com/paper/2103.12693/) (2021)
- [Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering](https://mlsanity.com/paper/2101.00774/) (2021)
- [Calibrating Sequence likelihood Improves Conditional Language Generation](https://mlsanity.com/paper/2210.00045/) (2022)
- [Improving Question Answering Model Robustness with Synthetic Adversarial Data Generation](https://mlsanity.com/paper/2104.08678/) (2021)
- [CLIcK: A Benchmark Dataset of Cultural and Linguistic Intelligence in Korean](https://mlsanity.com/paper/2403.06412/) (2024)
- [Text Generation by Learning from Demonstrations](https://mlsanity.com/paper/2009.07839/) (2020)
- [When Search Engine Services meet Large Language Models: Visions and Challenges](https://mlsanity.com/paper/2407.00128/) (2024)
- [Zero-shot Fact Verification by Claim Generation](https://mlsanity.com/paper/2105.14682/) (2021)
- [IndicNLG Benchmark: Multilingual Datasets for Diverse NLG Tasks in Indic Languages](https://mlsanity.com/paper/2203.05437/) (2022)
- [Guiding the Growth: Difficulty-Controllable Question Generation through Step-by-Step Rewriting](https://mlsanity.com/paper/2105.11698/) (2021)
- [Conditional Generation Net for Medication Recommendation](https://mlsanity.com/paper/2202.06588/) (2022)
- [Learning to Answer Visual Questions from Web Videos](https://mlsanity.com/paper/2205.05019/) (2022)
- [A Well-Composed Text is Half Done! Composition Sampling for Diverse Conditional Generation](https://mlsanity.com/paper/2203.15108/) (2022)
- [Generative Language Models for Paragraph-Level Question Generation](https://mlsanity.com/paper/2210.03992/) (2022)
- [Varifocal Question Generation for Fact-checking](https://mlsanity.com/paper/2210.12400/) (2022)
- [It is AI's Turn to Ask Humans a Question: Question-Answer Pair Generation for Children's Story Books](https://mlsanity.com/paper/2109.03423/) (2021)
- [Leveraging Large Language Models for Learning Complex Legal Concepts through Storytelling](https://mlsanity.com/paper/2402.17019/) (2024)
- [Question Generation for Reading Comprehension Assessment by Modeling How and What to Ask](https://mlsanity.com/paper/2204.02908/) (2022)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [Augmenting Pre-trained Language Models with QA-Memory for Open-Domain Question Answering](https://mlsanity.com/paper/2204.04581/) (2022)
- [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://mlsanity.com/paper/2211.01482/) (2022)
- [QGEval: Benchmarking Multi-dimensional Evaluation for Question Generation](https://mlsanity.com/paper/2406.05707/) (2024)
- [Shortcomings of Question Answering Based Factuality Frameworks for Error Localization](https://mlsanity.com/paper/2210.06748/) (2022)
- [An Empirical Comparison of LM-based Question and Answer Generation Methods](https://mlsanity.com/paper/2305.17002/) (2023)
- [InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification](https://mlsanity.com/paper/2401.16475/) (2024)
- [Synthetic Question Value Estimation for Domain Adaptation of Question Answering](https://mlsanity.com/paper/2203.08926/) (2022)
- [Automatic Database Configuration Debugging using Retrieval-Augmented Language Models](https://mlsanity.com/paper/2412.07548/) (2024)
- [Diversify Question Generation with Retrieval-Augmented Style Transfer](https://mlsanity.com/paper/2310.14503/) (2023)
- [Leaf: Multiple-Choice Question Generation](https://mlsanity.com/paper/2201.09012/) (2022)
- [QRelScore: Better Evaluating Generated Questions with Deeper Understanding of Context-aware Relevance](https://mlsanity.com/paper/2204.13921/) (2022)

## 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.