Document-level information extraction (IE) is a crucial task in natural language processing (NLP). This paper conducts a systematic review of recent document-level IE literature. In addition, we conduct a thorough error analysis with current state-of-the-art algorithms and identify their limitations as well as the remaining challenges for the task of document-level IE. According to our findings, labeling noises, entity coreference resolution, and lack of reasoning, severely affect the performance of document-level IE. The objective of this survey paper is to provide more insights and help NLP researchers to further enhance document-level IE performance.
@article{arxiv.2309.13249,
title = {A Survey of Document-Level Information Extraction},
author = {Hanwen Zheng and Sijia Wang and Lifu Huang},
journal= {arXiv preprint arXiv:2309.13249},
year = {2023}
}