Summary of A Survey on Neural Question Generation: Methods, Applications, and Prospects, by Shasha Guo et al.
A Survey on Neural Question Generation: Methods, Applications, and Prospects
by Shasha Guo, Lizi Liao, Cuiping Li, Tat-Seng Chua
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This survey presents a comprehensive overview of advancements in Neural Question Generation (NQG), a field that leverages neural networks to generate relevant questions from diverse inputs like knowledge bases, texts, and images. The paper begins by outlining the background of NQG, including problem formulation, benchmark datasets, evaluation metrics, and notable applications. It then categorizes NQG approaches into three categories: structured NQG using organized data sources, unstructured NQG focusing on loosely structured inputs like texts or visual content, and hybrid NQG drawing on diverse input modalities. The paper analyzes distinct neural network models tailored for each category, discussing their strengths and limitations. The survey concludes with a forward-looking perspective on the trajectory of NQG, identifying emergent research trends and prospective developmental paths. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how computers can generate questions based on information they learn from texts, images, and other sources. Researchers have been working on this task called Neural Question Generation (NQG) to help computers understand what we’re asking them. The paper looks at different ways people are trying to solve this problem, like using organized data or learning from unstructured text. It also talks about the strengths and weaknesses of these approaches. Overall, the paper is looking forward to seeing how NQG can continue to improve and be used in new and exciting ways. |
Keywords
» Artificial intelligence » Neural network
