Generating News-Centric Crossword Puzzles As A Constraint Satisfaction and Optimization Problem
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Abstract (arXiv)
Crossword puzzles have traditionally served not only as entertainment but also as an educational tool that can be used to acquire vocabulary and language proficiency. One strategy to enhance the educational purpose is personalization, such as including more words on a particular topic. This paper focuses on the case of encouraging people's interest in news and proposes a framework for automatically generating news-centric crossword puzzles. We designed possible scenarios and built a prototype as a constraint satisfaction and optimization problem, that is, containing as many news-derived words as possible. Our experiments reported the generation probabilities and time required under several conditions. The results showed that news-centric crossword puzzles can be generated even with few news-derived words. We summarize the current issues and future research directions through a qualitative evaluation of the prototype. This is the first proposal that a formulation of a constraint satisfaction and optimization problem can be beneficial as an educational application.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 6 of its 34 references that are in the map. First posted on arXiv 9 Aug 2023.
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References · 6
- 2017Learning to Ask: Neural Question Generation for Reading Comprehensioncited by 186
- 2020Deep Learning for Text Style Transfer: A Surveycited by 162
- 2019A Survey on Recent Advances in Named Entity Recognition from Deep Learning modelscited by 117
- 2019Addressing Semantic Drift in Question Generation for Semi-Supervised Question Answeringcited by 63
- 2019DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling Distributed Practice of Skillscited by 27
- 2022Towards Process-Oriented, Modular, and Versatile Question Generation that Meets Educational Needscited by 19
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