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.
@article{arxiv.2306.00177,
title = {Contrastive Hierarchical Discourse Graph for Scientific Document Summarization},
author = {Haopeng Zhang and Xiao Liu and Jiawei Zhang},
journal= {arXiv preprint arXiv:2306.00177},
year = {2023}
}