A Context-Aware Neural Approach for Explainable Citation Link Prediction

Abstract

Citations have become an integral part of scientific publications. They play a crucial role in supporting authors’ claims throughout a scientific paper. However, citing related work is a challenging and laborious task, especially for novice researchers who are not much familiar with the literature and have little or no experience in writing citation text. In this work, we study the task of Citation Link Prediction and propose a novel neural architecture called ExCite, that predicts the existence of a citation link between a pair of scientific documents within a given context. More importantly, it also generates the corresponding citation text at the same time. For this purpose, ExCite leverages diverse role-based views of the documents to learn robust document representations. The proposed model achieves state-of-the-art performance on both citation link prediction and citation text generation subtasks. We performed an extensive set of experiments to show the effectiveness of each module in the proposed neural architecture and evaluated our explanations using a wide range of state-of-the-art automatic evaluation metrics. By performing qualitative and quantitative analyses, we showed that ExCite is capable of generating high-quality citation text that is highly coherent with the citation context.