# Form 10-Q Itemization

arXiv: https://arxiv.org/abs/2104.11783 · HTML: https://mlsanity.com/paper/2104.11783/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2021.

## Built on by (most cited first, 10 of 10)

- [AFTer-UNet: Axial Fusion Transformer UNet for Medical Image Segmentation](https://mlsanity.com/paper/2110.10403/) (2021)
- [Large Language Models for Mobility Analysis in Transportation Systems: A Survey on Forecasting Tasks](https://mlsanity.com/paper/2405.02357/) (2024)
- [Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural Networks](https://mlsanity.com/paper/2301.13492/) (2023)
- [Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network](https://mlsanity.com/paper/2302.00873/) (2023)
- [A News-based Machine Learning Model for Adaptive Asset Pricing](https://mlsanity.com/paper/2106.07103/) (2021)
- [Clustering Structure of Microstructure Measures](https://mlsanity.com/paper/2107.02283/) (2021)
- [The Adaptive Multi-Factor Model and the Financial Market](https://mlsanity.com/paper/2107.14410/) (2021)
- [An Efficient Group-based Search Engine Marketing System for E-Commerce](https://mlsanity.com/paper/2106.12700/) (2021)
- [U-Net Convolutional Network for Recognition of Vessels and Materials in Chemistry Lab](https://mlsanity.com/paper/2109.05585/) (2021)
- [DIAMANT: Dual Image-Attention Map Encoders For Medical Image Segmentation](https://mlsanity.com/paper/2304.14571/) (2023)

## Abstract (arXiv)

The quarterly financial statement, or Form 10-Q, is one of the most frequently required filings for US public companies to disclose financial and other important business information. Due to the massive volume of 10-Q filings and the enormous variations in the reporting format, it has been a long-standing challenge to retrieve item-specific information from 10-Q filings that lack machine-readable hierarchy. This paper presents a solution for itemizing 10-Q files by complementing a rule-based algorithm with a Convolutional Neural Network (CNN) image classifier. This solution demonstrates a pipeline that can be generalized to a rapid data retrieval solution among a large volume of textual data using only typographic items. The extracted textual data can be used as unlabeled content-specific data to train transformer models (e.g., BERT) or fit into various field-focus natural language processing (NLP) applications.