research-article

FinSBD-2021: The 3rd Shared Task on Structure Boundary Detection in Unstructured Text in the Financial Domain

Authors:

Abderrahim Ait-Azzi,

Juyeon KangAuthors Info & Claims

WWW '21: Companion Proceedings of the Web Conference 2021

Pages 276 - 279

https://doi.org/10.1145/3442442.3451378

Published: 03 June 2021 Publication History

Abstract

Document processing is a foundational pre-processing task in natural language application applied in the financial domain. In this paper, we present the result of FinSBD-3, the 3rd shared task on Structure Boundary Detection in unstructured text in the financial domain. The shared task is organized as part of the 1st Workshop on Financial Technology on the Web. Participants were asked to create system detecting the boundaries of elements in unstructured text extracted from financial PDF. This edition extends the previous shared tasks by adding boundaries of visual elements such as tables, figures, page headers and page footers; on top of sentences, lists and list items which were already present in previous edition of the shared tasks.

References

[1]

2017. Multi-Scale Multi-Task FCN for Semantic Page Segmentation and Table Detection. Proceedings of the International Conference on Document Analysis and Recognition, ICDAR 1, 1, 254–261.

[2]

2020. DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers. 91–96. https://www.aclweb.org/anthology/2020.sdp-1.10

[3]

Abderrahim Ait Azzi, Houda Bouamor, and Sira Ferradans. 2019. The finsbd-2019 shared task: Sentence boundary detection in pdf noisy text in the financial domain. In Proceedings of the First Workshop on Financial Technology and Natural Language Processing. 74–80.

[4]

Cordula Guder Sebastian Brarda-Steffen Bickel Johannes Höhne Jean Baptiste Faddoul Anoop R Katti, Christian Reisswig. 2018. Chargrid: Towards Understanding 2D Documents. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, 4459–4469. https://doi.org/10.18653/v1/D18-1476

[5]

Azka Gilani, Shah Rukh Qasim, Imran Malik, and Faisal Shafait. 2017. Table Detection Using Deep Learning. 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR), 771–776. https://doi.org/10.1109/ICDAR.2017.131

[6]

L. Hao, L. Gao, X. Yi, and Z. Tang. 2016. A Table Detection Method for PDF Documents Based on Convolutional Neural Networks. 12th IAPR Workshop on Document Analysis Systems (DAS) IEEE Computer Society, 287–292.

[7]

Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing. (2017).

[8]

Glenn Jocher. [n.d.]. ([n. d.]). https://doi.org/10.5281/zenodo.4154370

[9]

Tien Dung Le. 2021. taka at the FinSBD-3 task: Tables and Figures Extraction using Object Detection Techniques. In Proceedings of the 1st Workshop on Financial Technology on the Web.

Digital Library

[10]

Ryoya Yamada Manabu Ohta. 2019. A Cell-detection-based Table-structure Recognition Method. In Proceedings of the ACM Symposium on Document Engineering 2019.

[11]

Ermelinda Oro Max Göbel, Tamir Hassan. 2013. ICDAR 2013 Table Competition. In Proceedings of the 2013 12th International Conference on Document Analysis and. 1449–1453. https://doi.org/10.1109/ICDAR.2013.292

[12]

Ivo Wolf Andreas Dengel Sebastian Schreiber, Stefan Agne and Sheraz Ahmed. 2017. DeepDeSRT: Deep learning for detection and structure recognition of tables in document images. In Proceedings of the 14th International Conference on Document Analysis and Recognition (ICDAR). 1162–1167.

[13]

Ke Tian and Hua Chen. 2021. aiai at the FinSBD3-shared task: Structure Boundary Detection of Noisy Financial Texts in English and French Using Data Augmentation and Hybrid Deep Learning model. In Proceedings of the 1st Workshop on Financial Technology on the Web.

[14]

Abderrahim Ait Azzi Dialekti Valsamou-Stanislawski Willy AU, Bianca Chong. 2020. FinSBD-2020: The 2nd Shared Task on Sentence Boundary Detection in Unstructured Text in the Financial Domain. In Proceedings of the Second Workshop on Financial Technology and Natural Language Processing. 47–54. https://www.aclweb.org/anthology/2020.finnlp-1.8

[15]

Antonio Jimeno Yepes Xu Zhong, Jianbin Tang. 2019. PubLayNet: Largest Dataset Ever for Document Layout Analysis. In Proceedings of the 15th International Conference on Document Analysis and Recognition (ICDAR). 1015–1022.

[16]

Xiao Yang, Ersin Yumer, Paul Asente, Mike Kraley, Daniel Kifer, and C. Lee Giles. 2017. Learning to extract semantic structure from documents using multimodal fully convolutional neural networks. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2017, 4342–4351. https://doi.org/10.1109/CVPR.2017.462

Cited By

Martínez-Rojas ALópez-Carnicer JGonzález-Enríquez JJiménez-Ramírez ASánchez-Oliva J(2023)Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature ReviewConfluence of Artificial Intelligence and Robotic Process Automation10.1007/978-981-19-8296-5_5(95-131)Online publication date: 14-Mar-2023
https://doi.org/10.1007/978-981-19-8296-5_5
Sasubilli SVerma M(2023)InFi-BERT 1.0: Transformer-Based Language Model for Indian Financial Volatility PredictionMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23633-4_10(128-138)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23633-4_10

FinSBD-2021: The 3rd Shared Task on Structure Boundary Detection in Unstructured Text in the Financial Domain
1. Computing methodologies
  1. Artificial intelligence

Recommendations

Utilizing sub-topical structure of documents for information retrieval
PIKM '11: Proceedings of the 4th workshop on Workshop for Ph.D. students in information & knowledge management

Text segmentation in natural language processing typically refers to the process of decomposing a document into constituent subtopics. Our work centers on the application of text segmentation techniques within information retrieval (IR) tasks. For ...
The ASSIN 2 Shared Task: A Quick Overview
Computational Processing of the Portuguese Language
Abstract
This paper offers a brief overview on the ASSIN 2, an evaluation shared task collocated with STIL 2019. ASSIN 2 covered two different but related tasks: Recognizing Textual Entailment (RTE), also known as Natural Language Inference (NLI), and ...
A visual analysis approach for data transformation via domain knowledge and intelligent models
Abstract
Industry benchmarking involves comparing and analyzing a company’s performance with other top-performing enterprises. PDF documents contain valuable corporate information, but their non-editable nature makes data extraction complex. This study ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

WWW '21: Companion Proceedings of the Web Conference 2021

April 2021

726 pages

ISBN:9781450383134

DOI:10.1145/3442442

Editors:
Jure Leskovec
Stanford
,
Marko Grobelnik
Jožef Stefan Institute
,
Marc Najork
Google
,
Jie Tang
Tsinghua University
,
Leila Zia
Wikimedia Foundation

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 03 June 2021

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article
Research
Refereed limited

Conference

WWW '21

Sponsor:

SIGWEB

WWW '21: The Web Conference 2021

April 19 - 23, 2021

Ljubljana, Slovenia

Acceptance Rates

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
98
Total Downloads

Downloads (Last 12 months)22
Downloads (Last 6 weeks)4

Reflects downloads up to 14 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Martínez-Rojas ALópez-Carnicer JGonzález-Enríquez JJiménez-Ramírez ASánchez-Oliva J(2023)Intelligent Document Processing in End-to-End RPA Contexts: A Systematic Literature ReviewConfluence of Artificial Intelligence and Robotic Process Automation10.1007/978-981-19-8296-5_5(95-131)Online publication date: 14-Mar-2023
https://doi.org/10.1007/978-981-19-8296-5_5
Sasubilli SVerma M(2023)InFi-BERT 1.0: Transformer-Based Language Model for Indian Financial Volatility PredictionMachine Learning and Principles and Practice of Knowledge Discovery in Databases10.1007/978-3-031-23633-4_10(128-138)Online publication date: 31-Jan-2023
https://doi.org/10.1007/978-3-031-23633-4_10

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Media

Figures

Other

Tables

View Table of Contents