  Numerical perspectives help people understand extreme and unfamiliar numbers
(e.g., \$330 billion is about \$1,000 per person in the United States). While
research shows perspectives to be helpful, generating them at scale is
challenging both because it is difficult to identify what makes some analogies
more helpful than others, and because what is most helpful can vary based on
the context in which a given number appears. Here we present and compare three
policies for large-scale perspective generation: a rule-based approach, a
crowdsourced system, and a model that uses Wikipedia data and semantic
similarity (via BERT embeddings) to generate context-specific perspectives. We
find that the combination of these three approaches dominates any single
method, with different approaches excelling in different settings and users
displaying heterogeneous preferences across approaches. We conclude by
discussing our deployment of perspectives in a widely-used online word
processor.
