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.

Related papers

• BoundingDocs: a Unified Dataset for Document Question Answering with Spatial Annotations [2.9798896492745537]
  We present a unified dataset for document Question-Answering (QA) We reformulate existing Document AI tasks, such as Information Extraction (IE), into a Question-Answering task. On the other hand, we release the OCR of all the documents and include the exact position of the answer to be found in the document image as a bounding box.
  arXiv  Detail & Related papers  (2025-01-06T21:46:22Z)
• Unified Multimodal Interleaved Document Representation for Retrieval [57.65409208879344]
  We propose a method that holistically embeds documents interleaved with multiple modalities. We merge the representations of segmented passages into one single document representation. We show that our approach substantially outperforms relevant baselines.
  arXiv  Detail & Related papers  (2024-10-03T17:49:09Z)
• On Task-personalized Multimodal Few-shot Learning for Visually-rich Document Entity Retrieval [59.25292920967197]
  Few-shot document entity retrieval (VDER) is an important topic in industrial NLP applications. FewVEX is a new dataset to boost future research in the field of entity-level few-shot VDER. We present a task-aware meta-learning based framework, with a central focus on achieving effective task personalization.
  arXiv  Detail & Related papers  (2023-11-01T17:51:43Z)
• DocumentNet: Bridging the Data Gap in Document Pre-Training [78.01647768018485]
  We propose a method to collect massive-scale and weakly labeled data from the web to benefit the training of VDER models. The collected dataset, named DocumentNet, does not depend on specific document types or entity sets. Experiments on a set of broadly adopted VDER tasks show significant improvements when DocumentNet is incorporated into the pre-training.
  arXiv  Detail & Related papers  (2023-06-15T08:21:15Z)
• Unifying Vision, Text, and Layout for Universal Document Processing [105.36490575974028]
  We propose a Document AI model which unifies text, image, and layout modalities together with varied task formats, including document understanding and generation. Our method sets the state-of-the-art on 9 Document AI tasks, e.g., document understanding and QA, across diverse data domains like finance reports, academic papers, and websites.
  arXiv  Detail & Related papers  (2022-12-05T22:14:49Z)
• VRDU: A Benchmark for Visually-rich Document Understanding [22.040372755535767]
  We identify the desiderata for a more comprehensive benchmark and propose one we call Visually Rich Document Understanding (VRDU) VRDU contains two datasets that represent several challenges: rich schema including diverse data types as well as hierarchical entities, complex templates including tables and multi-column layouts, and diversity of different layouts (templates) within a single document type. We design few-shot and conventional experiment settings along with a carefully designed matching algorithm to evaluate extraction results.
  arXiv  Detail & Related papers  (2022-11-15T03:17:07Z)
• Layout-Aware Information Extraction for Document-Grounded Dialogue: Dataset, Method and Demonstration [75.47708732473586]
  We propose a layout-aware document-level Information Extraction dataset, LIE, to facilitate the study of extracting both structural and semantic knowledge from visually rich documents. LIE contains 62k annotations of three extraction tasks from 4,061 pages in product and official documents. Empirical results show that layout is critical for VRD-based extraction, and system demonstration also verifies that the extracted knowledge can help locate the answers that users care about.
  arXiv  Detail & Related papers  (2022-07-14T07:59:45Z)
• Towards a Multi-modal, Multi-task Learning based Pre-training Framework for Document Representation Learning [5.109216329453963]
  We introduce Document Topic Modelling and Document Shuffle Prediction as novel pre-training tasks. We utilize the Longformer network architecture as the backbone to encode the multi-modal information from multi-page documents in an end-to-end fashion.
  arXiv  Detail & Related papers  (2020-09-30T05:39:04Z)
• SciREX: A Challenge Dataset for Document-Level Information Extraction [56.83748634747753]
  It is challenging to create a large-scale information extraction dataset at the document level. We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
  arXiv  Detail & Related papers  (2020-05-01T17:30:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.