Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
Haopeng Zhang, Xiao Liu, Jiawei Zhang · 2023
The extended structural context has made scientific paper summarization a challenging task.This paper proposes CHANGES, a contrastive hierarchical graph neural network for extractive scientific paper summarization.CHANGES represents a scientific paper with a hierarchical discourse graph and learns effective sentence representations with dedicated designed hierarchical graph information aggregation.We also propose a graph contrastive learning module to learn global theme-aware sentence representations.Extensive experiments on the PubMed and arXiv benchmark datasets prove the effectiveness of CHANGES and the importance of capturing hierarchical structure information in modeling scientific papers.