Capturing Relations between Scientific Papers: An Abstractive Model for Related Work Section Generation

Given a set of related publications, related work section generation aims to provide researchers with an overview of the specific research area by summarizing these works and introducing them in a logical order. Most of existing related work section generation models follow the inflexible extractive style, which directly extract sentences from multiple original papers to form a related work discussion. Hence, in this paper, we propose a Relationaware Related work Generator (RRG), which generates an abstractive related work section from multiple scientific papers in the same research area. Concretely, we propose a relationaware multi-document encoder that relates one document to another according to their content dependency in a relation graph. The relation graph and the document representation interact and are refined iteratively, complementing each other in the training process. We also contribute two public datasets composed of related work sections and their corresponding papers 1 . Extensive experiments on the two datasets show that the proposed model brings substantial improvements over several strong baselines. We hope that this work will promote advances in related work section generation task.