mlsanityData to 7 Oct 2026

A Survey of Document-Level Information Extraction

arXiv 2309.13249 ↗First posted 23 Sep 2023cs.CLcited by 2

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Abstract (arXiv)

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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 43 of its 60 references that are in the map. First posted on arXiv 23 Sep 2023.

Builds on

43 of 60 references.

References · 43

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31 more
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Citing papers per year: 2024: 1; 2025: 1.

2 papers.

  1. 2024AutoRE: Document-Level Relation Extraction with Large Language Modelscited by 16
  2. 2025Reasoning Pattern Matters: Learning to Reason without Human Rationalescited by 2