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.

本文介绍了通过自然语言文本解释两个科技文档之间关系的任务，提出了一个新的数据集和模型，探索使用科学信息提取系统的密集表示对提高解释性能的影响，并在自动和人类评估中证明了该模型的可行性和挑战。