  A common writing style for statistical results are the recommendations of the
American Psychology Association, known as APA-style. However, in practice,
writing styles vary as reports are not 100% following APA-style or parameters
are not reported despite being mandatory. In addition, the statistics are not
reported in isolation but in context of experimental conditions investigated
and the general topic. We address these challenges by proposing a flexible
pipeline STEREO based on active wrapper induction and unsupervised aspect
extraction. We applied our pipeline to the over 100,000 documents in the
CORD-19 dataset. It required only 0.25% of the corpus (about 500 documents) to
learn statistics extraction rules that cover 95% of the sentences in CORD-19.
The statistic extraction has nearly 100% precision on APA-conform and 95%
precision on non-APA writing styles. In total, we were able to extract 113k
reported statistics, of which only <1% is APA conform. We could extract in 46%
the correct conditions from APA-conform reports (30% for non-APA). The best
model for topic extraction achieves a precision of 75% on statistics reported
in APA style (73% for non-APA conform). We conclude that STEREO is a good
foundation for automatic statistic extraction and future developments for
scientific paper analysis. Particularly the extraction of non-APA conform
reports is important and allows applications such as giving feedback to authors
about what is missing and could be changed.
