Generating News-Centric Crossword Puzzles As A Constraint Satisfaction and Optimization Problem

TL;DR

This paper presents a novel framework for automatically generating news-centric crossword puzzles using constraint satisfaction and optimization techniques, aiming to enhance educational engagement with current news topics.

Contribution

It introduces the first formulation of news-centric crossword puzzle generation as a constraint satisfaction and optimization problem, demonstrating its feasibility and potential educational benefits.

Findings

Puzzles can be generated with few news words.

Generation is feasible within reasonable time.

Prototype shows promising results for educational use.

Abstract

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.