@inproceedings{saini-nayak-2025-effective,
    title = "Effective Modeling of Generative Framework for Document-level Relational Triple Extraction",
    author = "Saini, Pratik  and
      Nayak, Tapas",
    editor = "Gesese, Genet Asefa  and
      Sack, Harald  and
      Paulheim, Heiko  and
      Merono-Penuela, Albert  and
      Chen, Lihu",
    booktitle = "Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)",
    month = jan,
    year = "2025",
    address = "Abu Dhabi, UAE",
    publisher = "International Committee on Computational Linguistics",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2025.genaik-1.1/",
    pages = "1--12",
    abstract = "Document-level relation triple extraction (DocRTE) is a complex task that involves three key sub-tasks: entity mention extraction, entity clustering, and relation triple extraction. Past work has applied discriminative models to address these three sub-tasks, either by training them sequentially in a pipeline fashion or jointly training them. However, while end-to-end discriminative or generative models have proven effective for sentence-level relation triple extraction, they cannot be trivially extended to the document level, as they only handle relation extraction without addressing the remaining two sub-tasks, entity mention extraction or clustering. In this paper, we propose a three-stage generative framework leveraging a pre-trained BART model to address all three tasks required for document-level relation triple extraction. Tested on the widely used DocRED dataset, our approach outperforms previous generative methods and achieves competitive performance against discriminative models."
}