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In this paper, we propose such a system in the context of Wikipedia and evaluate it with Wikipedia readers and editors. Our solution builds upon the ArticlePlaceholder, a tool used in 14 under-resourced Wikipedia language versions, which displays structured data from the Wikidata knowledge base on empty Wikipedia pages. We train a neural network to generate an introductory sentence from the Wikidata triples shown by the ArticlePlaceholder, and explore how Wikipedia users engage with it. The evaluation, which includes an automatic, a judgement-based, and a task-based component, shows that the summary sentences score well in terms of perceived fluency and appropriateness for Wikipedia, and can help editors bootstrap new articles. It also hints at several potential implications of using NLG solutions in Wikipedia at large, including content quality, trust in technology, and algorithmic transparency.<\/jats:p>","DOI":"10.3233\/sw-210431","type":"journal-article","created":{"date-parts":[[2021,4,30]],"date-time":"2021-04-30T14:23:06Z","timestamp":1619792586000},"page":"163-194","source":"Crossref","is-referenced-by-count":5,"title":["Using natural language generation to bootstrap missing Wikipedia articles: A\u00a0human-centric perspective"],"prefix":"10.1177","volume":"13","author":[{"given":"Lucie-Aim\u00e9e","family":"Kaffee","sequence":"first","affiliation":[{"name":"School of Electronics and Computer Science, University of Southampton, UK. E-mail:\u00a0kaffee@soton.ac.uk"}]},{"given":"Pavlos","family":"Vougiouklis","sequence":"additional","affiliation":[{"name":"Huawei Technologies, UK. 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