While Wikipedia exists in 287 languages, its content is unevenly distributed among them. It is therefore of utmost social and cultural importance to focus efforts on languages whose speakers only have access to limited Wikipedia content. We investigate supporting communities by generating summaries for Wikipedia articles in underserved languages, given structured data as an input.
We focus on an important support for such summaries: ArticlePlaceholders, a dynamically generated content pages in underserved Wikipedias. They enable native speakers to access existing information in Wikidata. To extend those ArticlePlaceholders, we provide a system, which processes the triples of the KB as they are provided by the ArticlePlaceholder, and generate a comprehensible textual summary. This data-driven approach is employed with the goal of understanding how well it matches the communities’ needs on two underserved languages on the Web: Arabic, a language with a big community with disproportionate access to knowledge online, and Esperanto, an easily-acquainted, artificial language whose Wikipedia content is maintained by a small but devoted community. With the help of the Arabic and Esperanto Wikipedians, we conduct a study which evaluates not only the quality of the generated text, but also the usefulness of our end-system to any underserved Wikipedia version.