  Official government publications are key sources for understanding the
history of societies. Web publishing has fundamentally changed the scale and
processes by which governments produce and disseminate information.
Significantly, a range of web archiving programs have captured massive troves
of government publications. For example, hundreds of millions of unique U.S.
Government documents posted to the web in PDF form have been archived by
libraries to date. Yet, these PDFs remain largely unutilized and understudied
in part due to the challenges surrounding the development of scalable pipelines
for searching and analyzing them. This paper utilizes a Library of Congress
dataset of 1,000 government PDFs in order to offer initial approaches for
searching and analyzing these PDFs at scale. In addition to demonstrating the
utility of PDF metadata, this paper offers computationally-efficient machine
learning approaches to search and discovery that utilize the PDFs' textual and
visual features as well. We conclude by detailing how these methods can be
operationalized at scale in order to support systems for navigating millions of
PDFs.
