  In this paper, we propose a novel method for joint entity and relation
extraction from unstructured text by framing it as a conditional sequence
generation problem. In contrast to conventional generative information
extraction models that are left-to-right token-level generators, our approach
is \textit{span-based}. It generates a linearized graph where nodes represent
text spans and edges represent relation triplets. Our method employs a
transformer encoder-decoder architecture with pointing mechanism on a dynamic
vocabulary of spans and relation types. Our model can capture the structural
characteristics and boundaries of entities and relations through span
representations while simultaneously grounding the generated output in the
original text thanks to the pointing mechanism. Evaluation on benchmark
datasets validates the effectiveness of our approach, demonstrating competitive
results. Code is available at https://github.com/urchade/ATG.
