short-paper

Form 10-Q Itemization

Authors:

Lawrence Donohue,

Rui DaiAuthors Info & Claims

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

Pages 4817 - 4822

https://doi.org/10.1145/3459637.3481989

Published: 30 October 2021 Publication History

Abstract

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.

Supplementary Material

MP4 File (CIKM_Form_10Q_10min.mp4)

Presentation video of the Form 10-Q Itemization paper. It includes a brief review of problem statement, demo website interface, pipeline design and performance.

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Index Terms

Form 10-Q Itemization
1. Applied computing
  1. Document management and text processing
2. Information systems
  1. Information retrieval

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cover image ACM Conferences

CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management

October 2021

4966 pages

ISBN:9781450384469

DOI:10.1145/3459637

General Chairs:
Gianluca Demartini
The University of Queensland, Australia
,
Guido Zuccon
The University of Queensland, Australia
,
Program Chairs:
J. Shane Culpepper
RMIT University, Australia
,
Zi Huang
The University of Queensland, Australia
,
Hanghang Tong
University of Illinois at Urbana-Champaign, USA

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https://dl.acm.org/doi/10.1145/3543507.3583287
Yeganeh YFarshad AWeinberger PAhmadi SAdeli ENavab N(2023)Transformers Pay Attention to Convolutions Leveraging Emerging Properties of ViTs by Dual Attention-Image Network2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW)10.1109/ICCVW60793.2023.00244(2296-2307)Online publication date: 2-Oct-2023
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https://doi.org/10.1080/26941899.2023.2187895
Bi WXu BSun XWang ZShen HCheng XZhang ARangwala H(2022)Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph with Hierarchical Graph Neural NetworksProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539129(2712-2720)Online publication date: 14-Aug-2022
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