Text Mining Business Policy Documents: Applied Data Science in Finance

In a time when the employment of natural language processing techniques in domains such as
biomedicine, national security, finance, and law is flourishing, this study takes a deep look at its
application in policy documents. Besides providing an overview of the current state of the literature
that treats these concepts, the authors implement a set of natural language processing techniques on
internal bank policies. The implementation of these techniques, together with the results that derive
from the experiments and expert evaluation, introduce a meta-algorithmic modelling framework for
processing internal business policies. This framework relies on three natural language processing
techniques, namely information extraction, automatic summarization, and automatic keyword
extraction. For the reference extraction and keyword extraction tasks, the authors calculated precision,
recall, and F-scores. For the former, the researchers obtained 0.99, 0.84, and 0.89; for the latter, this
research obtained 0.79, 0.87, and 0.83, respectively. Finally, the summary extraction approach was
positively evaluated using a qualitative assessment.