  Form 10-Q, the quarterly financial statement, is one of the most crucial
filings for US public firms to disclose their financial and other relevant
business operation information. Due to the gigantic number of 10-Q filings
prevailing in the market for each quarter and diverse variations in the
implementation of format given company-specific nature, it has long been a
problem in the field to provide a generalized way to dissect and retrieve the
itemized information. In this paper, we create a tool to itemize 10-Q filings
using multi-stage processes, blending a rule-based algorithm with a CNN deep
learning model. The implementation is an integrated pipeline which provides a
solution to the item retrieval on a large scale. This would enable cross
sectional and longitudinal textual analysis on massive number of companies.
