  We present an approach for automatic extraction of measured values from the
astrophysical literature, using the Hubble constant for our pilot study. Our
rules-based model -- a classical technique in natural language processing --
has successfully extracted 298 measurements of the Hubble constant, with
uncertainties, from the 208,541 available arXiv astrophysics papers. We have
also created an artificial neural network classifier to identify papers in
arXiv which report novel measurements. From the analysis of our results we find
that reporting measurements with uncertainties and the correct units is
critical information when distinguishing novel measurements in free text. Our
results correctly highlight the current tension for measurements of the Hubble
constant and recover the $3.5\sigma$ discrepancy -- demonstrating that the tool
presented in this paper is useful for meta-studies of astrophysical
measurements from a large number of publications.
