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Template-free Data-to-Text Generation of Finnish Sports News

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

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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, s. 242--252

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Publicerad: 2019-10-02

ISBN: 978-91-7929-995-8

ISSN: 1650-3686 (tryckt), 1650-3740 (online)

Abstract

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.

Nyckelord

Data-to-text Text Generation News Generation Corpus Annotation

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