Wikipedia is one of the richest knowledge sources on the Web today. In order
to facilitate navigating, searching, and maintaining its content, Wikipedia's
guidelines state that all articles should be annotated with a so-called short
description indicating the article's topic (e.g., the short description of beer
is "Alcoholic drink made from fermented cereal grains"). Nonetheless, a large
fraction of articles (ranging from 10.2% in Dutch to 99.7% in Kazakh) have no
short description yet, with detrimental effects for millions of Wikipedia
users. Motivated by this problem, we introduce the novel task of automatically
generating short descriptions for Wikipedia articles and propose Descartes, a
multilingual model for tackling it. Descartes integrates three sources of
information to generate an article description in a target language: the text
of the article in all its language versions, the already-existing descriptions
(if any) of the article in other languages, and semantic type information
obtained from a knowledge graph. We evaluate a Descartes model trained for
handling 25 languages simultaneously, showing that it beats baselines
(including a strong translation-based baseline) and performs on par with
monolingual models tailored for specific languages. A human evaluation on three
languages further shows that the quality of Descartes's descriptions is largely
indistinguishable from that of human-written descriptions; e.g., 91.3% of our
English descriptions (vs. 92.1% of human-written descriptions) pass the bar for
inclusion in Wikipedia, suggesting that Descartes is ready for production, with
the potential to support human editors in filling a major gap in today's
Wikipedia across languages.

该研究提出了一种自动生成多语言维基百科文章简短描述的方法，该方法整合了文章的多种语言版本、已有的简短描述和知识图谱中的语义类型信息，其生成的文章描述已通过人类评价的测试。