This paper introduces the DocILE benchmark with the largest dataset of
business documents for the tasks of Key Information Localization and Extraction
and Line Item Recognition. It contains 6.7k annotated business documents, 100k
synthetically generated documents, and nearly~1M unlabeled documents for
unsupervised pre-training. The dataset has been built with knowledge of domain-
and task-specific aspects, resulting in the following key features: (i)
annotations in 55 classes, which surpasses the granularity of previously
published key information extraction datasets by a large margin; (ii) Line Item
Recognition represents a highly practical information extraction task, where
key information has to be assigned to items in a table; (iii) documents come
from numerous layouts and the test set includes zero- and few-shot cases as
well as layouts commonly seen in the training set. The benchmark comes with
several baselines, including RoBERTa, LayoutLMv3 and DETR-based Table
Transformer. These baseline models were applied to both tasks of the DocILE
benchmark, with results shared in this paper, offering a quick starting point
for future work. The dataset and baselines are available at
this https URL.

本文介绍了 DocILE 数据集及其应用，它包括 6.7K 个注释的商业文件、100K 个合成文件和近 1M 个未标记的文件，旨在为关键信息定位和提取以及行项目识别等任务提供基准，并提供了几个基线模型，包括 RoBERTa、LayoutLMv3 和 DETR-based Table Transformer。

DocILE 文件信息定位与提取基准测试

DocILE Benchmark for Document Information Localization and Extraction

Information extraction from semi-structured documents is crucial for
frictionless business-to-business (B2B) communication. While machine learning
problems related to Document Information Extraction (IE) have been studied for
decades, many common problem definitions and benchmarks do not reflect
domain-specific aspects and practical needs for automating B2B document
communication. We review the landscape of Document IE problems, datasets and
benchmarks. We highlight the practical aspects missing in the common
definitions and define the Key Information Localization and Extraction (KILE)
and Line Item Recognition (LIR) problems. There is a lack of relevant datasets
and benchmarks for Document IE on semi-structured business documents as their
content is typically legally protected or sensitive. We discuss potential
sources of available documents including synthetic data.

本文研究信息提取的问题，探讨了文档信息提取问题、数据集、基准测试等方面，提出了机器学习中的关键信息定位、提取和条目识别问题，并批评了目前缺乏半结构化商务文档的相关数据集和基准测试。