The Financial Document Structure Extraction Shared Task (FinTOC 2022)
This paper describes the FinTOC-2022 Shared Task on the structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 4th Financial Narrative Processing Workshop (FNP 2022), held jointly at The 13th Edition of the Language R...
| Autores: | , , , , , , , , |
|---|---|
| Tipo de recurso: | capítulo de libro |
| Fecha de publicación: | 2022 |
| País: | España |
| Institución: | Universidad Autónoma de Madrid |
| Repositorio: | Biblos-e Archivo. Repositorio Institucional de la UAM |
| Idioma: | inglés |
| OAI Identifier: | oai:repositorio.uam.es:10486/711007 |
| Acceso en línea: | http://hdl.handle.net/10486/711007 |
| Access Level: | acceso abierto |
| Palabra clave: | Financial Data Annotation Document Structure Extraction Table-Of-Contents Extraction Machine Learning Filología |
| Sumario: | This paper describes the FinTOC-2022 Shared Task on the structure extraction from financial documents, its participants results and their findings. This shared task was organized as part of The 4th Financial Narrative Processing Workshop (FNP 2022), held jointly at The 13th Edition of the Language Resources and Evaluation Conference (LREC 2022), Marseille, France (El-Haj et al., 2022). This shared task aimed to stimulate research in systems for extracting table-of-contents (TOC) from investment documents (such as financial prospectuses) by detecting the document titles and organizing them hierarchically into a TOC. For the forth edition of this shared task, three subtasks were presented to the participants: one with English documents, one with French documents and the other one with Spanish documents. This year, we proposed a different and revised dataset for English and French compared to the previous editions of FinTOC and a new dataset for Spanish documents was added. The task attracted 6 submissions for each language from 4 teams, and the most successful methods make use of textual, structural and visual features extracted from the documents and propose classification models for detecting titles and TOCs for all of the subtasks |
|---|