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Automatically Generating Interesting Facts from Wikipedia Tables

Cong Yu
SIGMOD (2019)
Google Scholar


Modern search engines provide contextual information surrounding query entities beyond ``ten blue links'' in the form of knowledge cards. Among the various attributes displayed about entities there has been recent interest in providing trivia due to observed engagement rates. Obtaining such trivia at a large scale is, however, non-trivial: hiring professional content creators is expensive and extracting statements from the Web can result in unreliable or uninteresting facts. In this paper we show how fun facts can be mined from tables on the Web to provide a large volume of reliable and interesting content. We employ a template-based approach to generate statements that are postprocessed by workers. We show how to bootstrap and streamline the process for faster and cheaper task completion. However, the content contained in these tables is dynamic. Therefore, we address the problem of automatically maintaining templates when tables are updated.