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AggChecker: A Fact-Checking System for Text Summaries of Relational Data Sets

Saehan Jo
Immanuel Trummer
Weicheng Yu
Cong Yu
Daniel Liu
Niyati Mehta
VLDB (2019)
Google Scholar


We demonstrate AggChecker, a novel tool for verifying textual summaries of relational data sets. The system automatically verifies natural language claims about numerical aggregates against the underlying raw data. The system incorporates a combination of natural language processing, information retrieval, machine learning, and efficient query processing strategies. Each claim is translated into a semantically equivalent SQL query and evaluated against the database. Our primary goal is analogous to that of a spell-checker: to identify erroneous claims and provide guidance in correcting them. In this demonstration, we show that our system enables users to verify text summaries much more efficiently than a standard SQL interface.