# Neural Models for Key Phrase Detection and Question Generation

arXiv: https://arxiv.org/abs/1706.04560 · HTML: https://mlsanity.com/paper/1706.04560/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jun 2017.

## Built on by (most cited first, 9 of 9)

- [Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge](https://mlsanity.com/paper/2104.06828/) (2021)
- [ChainCQG: Flow-Aware Conversational Question Generation](https://mlsanity.com/paper/2102.02864/) (2021)
- [From Statistical Methods to Deep Learning, Automatic Keyphrase Prediction: A Survey](https://mlsanity.com/paper/2305.02579/) (2023)
- [Diverse and Specific Clarification Question Generation with Keywords](https://mlsanity.com/paper/2104.10317/) (2021)
- [To Adapt or to Annotate: Challenges and Interventions for Domain Adaptation in Open-Domain Question Answering](https://mlsanity.com/paper/2212.10381/) (2022)
- [SGG: Learning to Select, Guide, and Generate for Keyphrase Generation](https://mlsanity.com/paper/2105.02544/) (2021)
- [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://mlsanity.com/paper/2109.04689/) (2021)
- [On the Evaluation of Answer-Agnostic Paragraph-level Multi-Question Generation](https://mlsanity.com/paper/2203.04464/) (2022)
- [Exploring Answer Information Methods for Question Generation with Transformers](https://mlsanity.com/paper/2312.03483/) (2023)

## Abstract (arXiv)

We propose a two-stage neural model to tackle question generation from documents. First, our model estimates the probability that word sequences in a document are ones that a human would pick when selecting candidate answers by training a neural key-phrase extractor on the answers in a question-answering corpus. Predicted key phrases then act as target answers and condition a sequence-to-sequence question-generation model with a copy mechanism. Empirically, our key-phrase extraction model significantly outperforms an entity-tagging baseline and existing rule-based approaches. We further demonstrate that our question generation system formulates fluent, answerable questions from key phrases. This two-stage system could be used to augment or generate reading comprehension datasets, which may be leveraged to improve machine reading systems or in educational settings.