  Due to the large amount of daily scientific publications, it is impossible to
manually review each one. Therefore, an automatic extraction of key information
is desirable. In this paper, we examine STEREO, a tool for extracting
statistics from scientific papers using regular expressions. By adapting an
existing regular expression inclusion algorithm for our use case, we decrease
the number of regular expressions used in STEREO by about $33.8\%$. We reveal
common patterns from the condensed rule set that can be used for the creation
of new rules. We also apply STEREO, which was previously trained in the
life-sciences and medical domain, to a new scientific domain, namely
Human-Computer-Interaction (HCI), and re-evaluate it. According to our
research, statistics in the HCI domain are similar to those in the medical
domain, although a higher percentage of APA-conform statistics were found in
the HCI domain. Additionally, we compare extraction on PDF and LaTeX source
files, finding LaTeX to be more reliable for extraction.
