Summary of Realkie: Five Novel Datasets For Enterprise Key Information Extraction, by Benjamin Townsend et al.
RealKIE: Five Novel Datasets for Enterprise Key Information Extraction
by Benjamin Townsend, Madison May, Christopher Wells
First submitted to arxiv on: 29 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary RealKIE is a benchmark of five challenging datasets designed to advance key information extraction methods, with a focus on enterprise applications. The datasets include SEC S1 Filings, US Non-disclosure Agreements, UK Charity Reports, FCC Invoices, and Resource Contracts, each presenting unique challenges such as 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 legal data processing. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary RealKIE is a collection of challenging datasets that helps computers better understand important information from different types of documents. The documents are hard to analyze because they have poor text formatting, aren’t well-organized, and contain complex tables. This makes it difficult for computers to find the most important details. RealKIE provides a testing ground for computer programs that need to extract key information from these types of documents. |




