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。