  While Wikipedia exists in 287 languages, its content is unevenly distributed
among them. In this work, we investigate the generation of open domain
Wikipedia summaries in underserved languages using structured data from
Wikidata. To this end, we propose a neural network architecture equipped with
copy actions that learns to generate single-sentence and comprehensible textual
summaries from Wikidata triples. We demonstrate the effectiveness of the
proposed approach by evaluating it against a set of baselines on two languages
of different natures: Arabic, a morphological rich language with a larger
vocabulary than English, and Esperanto, a constructed language known for its
easy acquisition.
