A systematic review of relation extraction task since the emergence of Transformers

TL;DR

This paper systematically reviews relation extraction research since Transformers emerged, analyzing publications, datasets, and models to identify trends, challenges, and future directions in the field.

Contribution

It provides a comprehensive overview of methodological advances, benchmark resources, and integration techniques in relation extraction since 2019.

Findings

Identification of key trends and limitations in RE research

Compilation of 34 surveys, 64 datasets, and 104 models

Highlighting open challenges and future research directions

Abstract

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.