Jenna Kanerva
TurkuNLP, Department of Future Technologies, University of Turku, Finland
Samuel Rönnqvist
TurkuNLP, Department of Future Technologies, University of Turku, Finland
Riina Kekki
TurkuNLP, Department of Future Technologies, University of Turku, Finland
Tapio Salakoski
TurkuNLP, Department of Future Technologies, University of Turku, Finland
Filip Ginter
TurkuNLP, Department of Future Technologies, University of Turku, Finland
Ladda ner artikel
Ingår i: Proceedings of the 22nd Nordic Conference on Computational Linguistics (NoDaLiDa), September 30 - October 2, Turku, Finland
Linköping Electronic Conference Proceedings 167:25, s. 242--252
NEALT Proceedings Series 42:25, p. 242--252
Publicerad: 2019-10-02
ISBN: 978-91-7929-995-8
ISSN: 1650-3686 (tryckt), 1650-3740 (online)
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-to-text 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.
Inga referenser tillgängliga
