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

The lack of data for information extraction (IE) from semi-structured
business documents is a real problem for the IE community. Publications relying
on large-scale datasets use only proprietary, unpublished data due to the
sensitive nature of such documents. Publicly available datasets are mostly
small and domain-specific. The absence of a large-scale public dataset or
benchmark hinders the reproducibility and cross-evaluation of published
methods. The DocILE 2023 competition, hosted as a lab at the CLEF 2023
conference and as an ICDAR 2023 competition, will run the first major benchmark
for the tasks of Key Information Localization and Extraction (KILE) and Line
Item Recognition (LIR) from business documents. With thousands of annotated
real documents from open sources, a hundred thousand of generated synthetic
documents, and nearly a million unlabeled documents, the DocILE lab comes with
the largest publicly available dataset for KILE and LIR. We are looking forward
to contributions from the Computer Vision, Natural Language Processing,
Information Retrieval, and other communities. The data, baselines, code and
up-to-date information about the lab and competition are available at
this https URL.

该研究提供了一个大规模的公开数据集：DocILE，用于从半结构化商业文件中提取关键信息和识别项目行。该数据集将用于业界的基准测试，并鼓励计算机视觉、自然语言处理和信息检索等领域的贡献。