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

• URL: http://arxiv.org/abs/2309.16396v3
• Date: Thu, 12 Oct 2023 13:39:39 GMT
• Title: A Comprehensive Survey of Document-level Relation Extraction (2016-2023)
• Authors: Julien Delaunay, Hanh Thi Hong Tran, Carlos-Emiliano Gonz\'alez-Gallardo, Georgeta Bordea, Nicolas Sidere, Antoine Doucet
• Abstract summary: Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction.
• Score: 3.0204640945657326
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Document-level relation extraction (DocRE) is an active area of research in natural language processing (NLP) concerned with identifying and extracting relationships between entities beyond sentence boundaries. Compared to the more traditional sentence-level relation extraction, DocRE provides a broader context for analysis and is more challenging because it involves identifying relationships that may span multiple sentences or paragraphs. This task has gained increased interest as a viable solution to build and populate knowledge bases automatically from unstructured large-scale documents (e.g., scientific papers, legal contracts, or news articles), in order to have a better understanding of relationships between entities. This paper aims to provide a comprehensive overview of recent advances in this field, highlighting its different applications in comparison to sentence-level relation extraction.

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