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, nontrivial: 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.

Logic2Text: High-Fidelity Natural Language Generation from Logical Forms

“…But the reports are still limited to superficial restatements of table records, with very few involving logical inference. Korn et al (2019) investigate generation of interesting trivia from superlative wikipedia tables. Chen et al (2020) propose the task of generating arbitrary sentences with logical inference from the table.…”

Hierarchical Trivia Fact Extraction from Wikipedia Articles

“…In addition, their algorithm only targets at the people domain because other domains do not necessarily contain trivia categories. Korn et al (2019) focused on Wikipedia's superlative tables as a natural source of interesting facts. The rows in the tables are sorted as the ranking of entities, based on the corresponding value such as the building's height.…”

“…One piece of information presented in the knowledge panel can be a trivia fact about the given entity, which contributes to the effective user engagement (Tsurel et al, 2017). For example, the Google search engine provides a trivia fact for a given entity to attract users' attention (Korn et al, 2019). Successfully attracting users' attention can facilitate the users to revisit the search engine (O'Brien and Toms, 2008).…”

Interpreting Open-Domain Modifiers: Decomposition of Wikipedia Categories into Disambiguated Property-Value Pairs

“…As it extracts semantic annotations over opendomain concepts (namely, over categories from Wikipedia), the proposed method falls under the area of open-domain information extraction (Ernst et al, 2018;Qu et al, 2018;Sun et al, 2018;Zhu et al, 2019;Zhan and Zhao, 2020;Dash et al, 2020;Cao et al, 2020). Previous work in that area often uses Wikipedia data (Tsurel et al, 2017;Konovalov et al, 2017;Korn et al, 2019;Bornemann et al, 2020).…”