# Improving Question Generation with Multi-level Content Planning

arXiv: https://arxiv.org/abs/2310.13512 · HTML: https://mlsanity.com/paper/2310.13512/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge- and tool-augmented reasoning, one of 17 directions in LLM reasoning.
- Builds on 4 of its 10 references that are in the map.
- First posted on arXiv 20 Oct 2023.

## Placed in

- LLM reasoning / [Knowledge- and tool-augmented reasoning](https://mlsanity.com/lr/knowledge-and-tool-augmented-reasoning/)

## Builds on (most cited first, 4 of 4)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Improving Neural Question Generation using Answer Separation](https://mlsanity.com/paper/1809.02393/) (2018)
- [Learning to Generate Questions by Learning What not to Generate](https://mlsanity.com/paper/1902.10418/) (2019)
- [Stronger Transformers for Neural Multi-Hop Question Generation](https://mlsanity.com/paper/2010.11374/) (2020)

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

- [A Survey on Neural Question Generation: Methods, Applications, and Prospects](https://mlsanity.com/paper/2402.18267/) (2024)
- [Multi-Hop Question Generation via Dual-Perspective Keyword Guidance](https://mlsanity.com/paper/2505.15299/) (2025)
- [Comparison of Large Language Models for Generating Contextually Relevant Questions](https://mlsanity.com/paper/2407.20578/) (2024)
- [HopWeaver: Cross-Document Synthesis of High-Quality and Authentic Multi-Hop Questions](https://mlsanity.com/paper/2505.15087/) (2025)
- [KCS: Diversify Multi-hop Question Generation with Knowledge Composition Sampling](https://mlsanity.com/paper/2508.20567/) (2025)
- [Momentum Posterior Regularization for Multi-hop Dense Retrieval](https://mlsanity.com/paper/2502.20399/) (2024)
- [Context Selection and Rewriting for Video-based Educational Question Generation](https://mlsanity.com/paper/2504.19406/) (2025)
- [Improving Zero-shot Sentence Decontextualisation with Content Selection and Planning](https://mlsanity.com/paper/2509.17921/) (2025)
- [Exploiting Intrinsic Duality for Multi-Hop Question Generation](https://mlsanity.com/paper/2608.00712/) (2026)

## Abstract (arXiv)

This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context. To mitigate this issue, we propose MultiFactor, a novel QG framework based on multi-level content planning. Specifically, MultiFactor includes two components: FA-model, which simultaneously selects key phrases and generates full answers, and Q-model which takes the generated full answer as an additional input to generate questions. Here, full answer generation is introduced to connect the short answer with the selected key phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model and Q-model are formalized as simple-yet-effective Phrase-Enhanced Transformers, our joint model for phrase selection and text generation. Experimental results show that our method outperforms strong baselines on two popular QG datasets. Our code is available at https://github.com/zeaver/MultiFactor.