Automatically Generating Interesting Facts from Wikipedia Tables

Abstract

Modern search engines provide contextual information surrounding query entities beyond ten blue links in the form of information cards. Among the various attributes displayed about entities there has been recent interest in providing fun facts. Obtaining such trivia at a large scale is, however, non-trivial: hiring professional content creators is expensive and extracting statements from the Web is prone to uninteresting, out-of-context and/or unreliable facts. In this paper we show how fun facts can be mined from superlative tables in Wikipedia, whose rows are ranked according to some statistics, to provide a large volume of reliable and interesting content. We employ a template-based approach to semi-automatically generate natural language statements as fun facts. 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 the pairing of templates to tables as the tables are updated over time. Fun facts produced by our work is now part of Google's production search results.