Automatically Generating Interesting Facts from Wikipedia Tables

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.

• Data Mining and Modeling