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Our goal is to find combinations of facts that optimally summarize data sets.We consider this problem in the context of voice query interfaces for simple,exploratory data analysis. Here, the system answers voice queries with a shortsummary of relevant data. Finding optimal voice data summaries iscomputationally expensive. Prior work in this domain has exploited sampling andincremental processing. Instead, we rely on a pre-processing stage generatingsummaries of data subsets in a batch operation. This step reduces run timeoverheads by orders of magnitude.  We present multiple algorithms for the pre-processing stage, realizingdifferent tradeoffs between optimality and data processing overheads. Weanalyze our algorithms formally and compare them experimentally with priormethods for generating voice data summaries. We report on multiple user studieswith a prototype system implementing our approach. Furthermore, we report oninsights gained from a public deployment of our system on the Google AssistantPlatform.

Optimally Summarizing Data by Small Fact Sets for Concise Answers to Voice Queries