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In this paper, we propose a novel method for joint entity and relationextraction from unstructured text by framing it as a conditional sequencegeneration problem. In contrast to conventional generative informationextraction models that are left-to-right token-level generators, our approachis \textit{span-based}. It generates a linearized graph where nodes representtext spans and edges represent relation triplets. Our method employs atransformer encoder-decoder architecture with pointing mechanism on a dynamicvocabulary of spans and relation types. Our model can capture the structuralcharacteristics and boundaries of entities and relations through spanrepresentations while simultaneously grounding the generated output in theoriginal text thanks to the pointing mechanism. Evaluation on benchmarkdatasets validates the effectiveness of our approach, demonstrating competitiveresults. Code is available at https://github.com/urchade/ATG.

An Autoregressive Text-to-Graph Framework for Joint Entity and Relation Extraction