# Ask To The Point: Open-Domain Entity-Centric Question Generation

arXiv: https://arxiv.org/abs/2310.14126 · HTML: https://mlsanity.com/paper/2310.14126/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 29 of its 45 references that are in the map.
- First posted on arXiv 21 Oct 2023.

## Builds on (most cited first, 29 of 29)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Know What You Don't Know: Unanswerable Questions for SQuAD](https://mlsanity.com/paper/1806.03822/) (2018)
- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Bidirectional Attention Flow for Machine Comprehension](https://mlsanity.com/paper/1611.01603/) (2016)
- [Learning to Ask: Neural Question Generation for Reading Comprehension](https://mlsanity.com/paper/1705.00106/) (2017)
- [Controllable Abstractive Summarization](https://mlsanity.com/paper/1711.05217/) (2017)
- [Transforming Question Answering Datasets Into Natural Language Inference Datasets](https://mlsanity.com/paper/1809.02922/) (2018)
- [CTRLsum: Towards Generic Controllable Text Summarization](https://mlsanity.com/paper/2012.04281/) (2020)
- [Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answering](https://mlsanity.com/paper/1909.06356/) (2019)
- [Zero-shot Fact Verification by Claim Generation](https://mlsanity.com/paper/2105.14682/) (2021)
- [A Survey of Multi-task Learning in Natural Language Processing: Regarding Task Relatedness and Training Methods](https://mlsanity.com/paper/2204.03508/) (2022)
- [Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus](https://mlsanity.com/paper/2002.00748/) (2020)
- [Controllable Neural Dialogue Summarization with Personal Named Entity Planning](https://mlsanity.com/paper/2109.13070/) (2021)
- [Educational Question Generation of Children Storybooks via Question Type Distribution Learning and Event-Centric Summarization](https://mlsanity.com/paper/2203.14187/) (2022)
- [Improving Unsupervised Question Answering via Summarization-Informed Question Generation](https://mlsanity.com/paper/2109.07954/) (2021)
- [Interconnected Question Generation with Coreference Alignment and Conversation Flow Modeling](https://mlsanity.com/paper/1906.06893/) (2019)
- [Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needs](https://mlsanity.com/paper/2205.00355/) (2022)
- [Question-type Driven Question Generation](https://mlsanity.com/paper/1909.00140/) (2019)
- [Evaluating Rewards for Question Generation Models](https://mlsanity.com/paper/1902.11049/) (2019)
- [Guiding Visual Question Generation](https://mlsanity.com/paper/2110.08226/) (2021)
- [Multi-Task Learning with Language Modeling for Question Generation](https://mlsanity.com/paper/1908.11813/) (2019)
- [Knowledge-enriched, Type-constrained and Grammar-guided Question Generation over Knowledge Bases](https://mlsanity.com/paper/2010.03157/) (2020)
- [Review-based Question Generation with Adaptive Instance Transfer and Augmentation](https://mlsanity.com/paper/1911.01556/) (2019)
- [Entity-Conditioned Question Generation for Robust Attention Distribution in Neural Information Retrieval](https://mlsanity.com/paper/2204.11373/) (2022)
- [Generating Self-Contained and Summary-Centric Question Answer Pairs via Differentiable Reward Imitation Learning](https://mlsanity.com/paper/2109.04689/) (2021)
- [Enhancing Pre-trained Models with Text Structure Knowledge for Question Generation](https://mlsanity.com/paper/2209.04179/) (2022)

## Built on by (most cited first, 2 of 2)

- [Certifying Knowledge Comprehension in LLMs](https://mlsanity.com/paper/2402.15929/) (2024)
- [Writing Like the Best: Exemplar-Based Expository Text Generation](https://mlsanity.com/paper/2505.18859/) (2025)

## Abstract (arXiv)

We introduce a new task called *entity-centric question generation* (ECQG), motivated by real-world applications such as topic-specific learning, assisted reading, and fact-checking. The task aims to generate questions from an entity perspective. To solve ECQG, we propose a coherent PLM-based framework GenCONE with two novel modules: content focusing and question verification. The content focusing module first identifies a focus as "what to ask" to form draft questions, and the question verification module refines the questions afterwards by verifying the answerability. We also construct a large-scale open-domain dataset from SQuAD to support this task. Our extensive experiments demonstrate that GenCONE significantly and consistently outperforms various baselines, and two modules are effective and complementary in generating high-quality questions.