Template-free Data-to-Text Generation of Finnish Sports News - Jenna Kanerva; Samuel Rönnqvist; Riina Kekki; Tapio Salakoski; Filip Ginter (Sweden, 2025)
News articles such as sports game reports are often thought to closely follow the underlying game statistics, but in practice they contain a notable amount of background knowledge, interpretation, insight into the game, and quotes that are not present in the official statistics. This poses a challenge for automated data-totext news generation with real-world news corpora as training data. We report on the development of a corpus of Finnish ice hockey news, edited to be suitable for training of end-to-end news generation methods, as well as demonstrate generation of text, which was judged by journalists to be relatively close to a viable product. The new dataset and system source code are available for research purposes.
Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30–October 2, Turku, Finland