BuDDIE: A Business Document Dataset for Multi-task Information Extraction

• URL: http://arxiv.org/abs/2404.04003v1
• Date: Fri, 5 Apr 2024 10:26:42 GMT
• Title: BuDDIE: A Business Document Dataset for Multi-task Information Extraction
• Authors: Ran Zmigrod, Dongsheng Wang, Mathieu Sibue, Yulong Pei, Petr Babkin, Ivan Brugere, Xiaomo Liu, Nacho Navarro, Antony Papadimitriou, William Watson, Zhiqiang Ma, Armineh Nourbakhsh, Sameena Shah,
• Abstract summary: BuDDIE is the first multi-task dataset of 1,665 real-world business documents. Our dataset consists of publicly available business entity documents from US state government websites.
• Score: 18.440587946049845
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The field of visually rich document understanding (VRDU) aims to solve a multitude of well-researched NLP tasks in a multi-modal domain. Several datasets exist for research on specific tasks of VRDU such as document classification (DC), key entity extraction (KEE), entity linking, visual question answering (VQA), inter alia. These datasets cover documents like invoices and receipts with sparse annotations such that they support one or two co-related tasks (e.g., entity extraction and entity linking). Unfortunately, only focusing on a single specific of documents or task is not representative of how documents often need to be processed in the wild - where variety in style and requirements is expected. In this paper, we introduce BuDDIE (Business Document Dataset for Information Extraction), the first multi-task dataset of 1,665 real-world business documents that contains rich and dense annotations for DC, KEE, and VQA. Our dataset consists of publicly available business entity documents from US state government websites. The documents are structured and vary in their style and layout across states and types (e.g., forms, certificates, reports, etc.). We provide data variety and quality metrics for BuDDIE as well as a series of baselines for each task. Our baselines cover traditional textual, multi-modal, and large language model approaches to VRDU.

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