Explaining relationships between scientific documents

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 that relationship in text. Successful solutions can help improve researcher efficiency in search and review. In this paper, we operationalize this task by using citing sentences as a proxy. We establish a large dataset for our task. 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 information extraction systems. We provide extensive automatic and human evaluations which show the promise of such models, and make clear the challenges for future work.