A Survey of Document-Level Information Extraction

Hanwen Zheng, Sijia Wang, Lifu Huang
9/23/2023
cs.CL

No confident code implementation found for this paper.

Abstract

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.

View on arXivView PDF

Code Implementations

No confident code match yet

We couldn't find an author-owned or strongly-evidenced community implementation for this paper. Any repos shown below are weak matches — verify before relying on them.

No code implementations found yet.

Know of an implementation? Let us know in the comments below!

Similar Papers

Workshop on Document Intelligence Understanding

83% match2023

Probing Representations for Document-level Event Extraction

82% match1 repo22023

Information Extraction from Documents: Question Answering vs Token Classification in real-world setups

81% match2023

Éclair -- Extracting Content and Layout with Integrated Reading Order for Documents

81% match2025

A Comprehensive Survey of Document-level Relation Extraction (2016-2023)

81% match2023

Rethinking Document-Level Relation Extraction: A Reality Check

81% match2023

Cite this paper

@article{zheng2023survey,
  title  = {A Survey of Document-Level Information Extraction},
  author = {Hanwen Zheng and Sijia Wang and Lifu Huang},
  year   = {2023},
  eprint = {2309.13249},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2309.13249v1}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2309.13249.svg)](https://codeofpaper.com/paper/2309.13249)

Discussion