@inproceedings{luu-etal-2021-explaining,
    title = "Explaining Relationships Between Scientific Documents",
    author = "Luu, Kelvin  and
      Wu, Xinyi  and
      Koncel-Kedziorski, Rik  and
      Lo, Kyle  and
      Cachola, Isabel  and
      Smith, Noah A.",
    booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
    month = aug,
    year = "2021",
    address = "Online",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2021.acl-long.166",
    doi = "10.18653/v1/2021.acl-long.166",
    pages = "2130--2144",
    abstract = "We address the task of explaining relationships between two scientific documents using natural language text. This task requires modeling the complex content of long technical documents, deducing a relationship between these documents, and expressing the details of that relationship in text. In addition to the theoretical interest of this task, successful solutions can help improve researcher efficiency in search and review. In this paper we establish a dataset of 622K examples from 154K documents. We pretrain a large language model to serve as the foundation for autoregressive approaches to the task. We explore the impact of taking different views on the two documents, including the use of dense representations extracted with scientific IE systems. We provide extensive automatic and human evaluations which show the promise of such models, but make clear challenges for future work.",
}