  In a citation graph, adjacent paper nodes share related scientific terms and
topics. The graph thus conveys unique structure information of document-level
relatedness that can be utilized in the paper summarization task, for exploring
beyond the intra-document information. In this work, we focus on leveraging
citation graphs to improve scientific paper extractive summarization under
different settings. We first propose a Multi-granularity Unsupervised
Summarization model (MUS) as a simple and low-cost solution to the task. MUS
finetunes a pre-trained encoder model on the citation graph by link prediction
tasks. Then, the abstract sentences are extracted from the corresponding paper
considering multi-granularity information. Preliminary results demonstrate that
citation graph is helpful even in a simple unsupervised framework. Motivated by
this, we next propose a Graph-based Supervised Summarization model (GSS) to
achieve more accurate results on the task when large-scale labeled data are
available. Apart from employing the link prediction as an auxiliary task, GSS
introduces a gated sentence encoder and a graph information fusion module to
take advantage of the graph information to polish the sentence representation.
Experiments on a public benchmark dataset show that MUS and GSS bring
substantial improvements over the prior state-of-the-art model.
