RealKIE: Five Novel Datasets for Enterprise Key Information Extraction
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866915534074806272 |
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| author | Townsend, Benjamin May, Madison Mackowiak, Katherine Wells, Christopher |
| author_facet | Townsend, Benjamin May, Madison Mackowiak, Katherine Wells, Christopher |
| contents | We introduce RealKIE, a benchmark of five challenging datasets aimed at advancing key information extraction methods, with an emphasis on enterprise applications. The datasets include a diverse range of documents including SEC S1 Filings, US Non-disclosure Agreements, UK Charity Reports, FCC Invoices, and Resource Contracts. Each presents unique challenges: poor text serialization, sparse annotations in long documents, and complex tabular layouts. These datasets provide a realistic testing ground for key information extraction tasks like investment analysis and contract analysis. In addition to presenting these datasets, we offer an in-depth description of the annotation process, document processing techniques, and baseline modeling approaches. This contribution facilitates the development of NLP models capable of handling practical challenges and supports further research into information extraction technologies applicable to industry-specific problems. The annotated data, OCR outputs, and code to reproduce baselines are available to download at https://indicodatasolutions.github.io/RealKIE/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_20101 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RealKIE: Five Novel Datasets for Enterprise Key Information Extraction Townsend, Benjamin May, Madison Mackowiak, Katherine Wells, Christopher Computation and Language Computer Vision and Pattern Recognition Machine Learning We introduce RealKIE, a benchmark of five challenging datasets aimed at advancing key information extraction methods, with an emphasis on enterprise applications. The datasets include a diverse range of documents including SEC S1 Filings, US Non-disclosure Agreements, UK Charity Reports, FCC Invoices, and Resource Contracts. Each presents unique challenges: poor text serialization, sparse annotations in long documents, and complex tabular layouts. These datasets provide a realistic testing ground for key information extraction tasks like investment analysis and contract analysis. In addition to presenting these datasets, we offer an in-depth description of the annotation process, document processing techniques, and baseline modeling approaches. This contribution facilitates the development of NLP models capable of handling practical challenges and supports further research into information extraction technologies applicable to industry-specific problems. The annotated data, OCR outputs, and code to reproduce baselines are available to download at https://indicodatasolutions.github.io/RealKIE/. |
| title | RealKIE: Five Novel Datasets for Enterprise Key Information Extraction |
| topic | Computation and Language Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2403.20101 |