This article presents a systematic review of relation extraction (RE) research since the advent of Transformer-based models. Using an automated framework to collect and annotate publications, we analyze 34 surveys, 64 datasets, and 104 models published between 2019 and 2024. The review highlights methodological advances, benchmark resources, and the integration of semantic web technologies. By consolidating results across multiple dimensions, the study identifies current trends, limitations, and open challenges, offering researchers and practitioners a comprehensive reference for understanding the evolution and future directions of RE.
@article{arxiv.2511.03610,
title = {A systematic review of relation extraction task since the emergence of Transformers},
author = {Ringwald Celian and Gandon and Fabien and Faron Catherine and Michel Franck and Abi Akl Hanna},
journal= {arXiv preprint arXiv:2511.03610},
year = {2025}
}
Comments
Submited at ACM-Computing Surveys + The resulting annotated Zotero bibliography : https://www.zotero.org/groups/6070963/scilex_re_systlitreview/library + SciLEx software: https://github.com/Wimmics/SciLEx