Generating News-Centric Crossword Puzzles As A Constraint Satisfaction
  and Optimization Problem
Kaito Majima Shotaro Ishihara
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.