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.

This paper highlights the importance of reading time for news media and evaluates the implementation methodology. The display of estimated reading time allows users to select and view articles that are appropriate for their situation. The simplest hypothesis for the implementation is that reading time correlates with text length. We analyzed real-world users of Japanese financial news and revealed that reading time does not strongly correlate with text length. Experiments also showed that a multimodal machine learning approach leads to a more accurate estimation. Specifically, fine-tuning neural networks that incorporated LSTM to process user history and BERT and Swin Transformer to acquire embeddings from the articles achieved the best results.

This paper proposes a human-in-the-loop framework to summarize news articles by sentence selection and compression. The system enumerates summary candidates by selecting N sentences representing the article based on a quantitative metric and then compressing each sentence by syntactic analysis. Experiments showed that the proposed system was able to extract the same topics as the human editor's work at the rate of 26 %. Even though the rate was not high enough, the proposed framework has the advantage that it is easy to incorporate the editor's intentions in the sentence selection and compression by giving weights. This approach not only has the potential to reduce the burden on editors, but can also contribute to giving them a new perspective in creating summaries.