  We present a novel natural language query interface, the AggChecker, aimed at
text summaries of relational data sets. The tool focuses on natural language
claims that translate into an SQL query and a claimed query result. Similar in
spirit to a spell checker, the AggChecker marks up text passages that seem to
be inconsistent with the actual data. At the heart of the system is a
probabilistic model that reasons about the input document in a holistic
fashion. Based on claim keywords and the document structure, it maps each text
claim to a probability distribution over associated query translations. By
efficiently executing tens to hundreds of thousands of candidate translations
for a typical input document, the system maps text claims to correctness
probabilities. This process becomes practical via a specialized processing
backend, avoiding redundant work via query merging and result caching.
Verification is an interactive process in which users are shown tentative
results, enabling them to take corrective actions if necessary.
  Our system was tested on a set of 53 public articles containing 392 claims.
Our test cases include articles from major newspapers, summaries of survey
results, and Wikipedia articles. Our tool revealed erroneous claims in roughly
a third of test cases. A detailed user study shows that users using our tool
are in average six times faster at checking text summaries, compared to generic
SQL interfaces. In fully automated verification, our tool achieves
significantly higher recall and precision than baselines from the areas of
natural language query interfaces and fact-checking.
