Computer Science > Computation and Language
arXiv:2306.00177 (cs)
[Submitted on 31 May 2023]
Title:Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
View a PDF of the paper titled Contrastive Hierarchical Discourse Graph for Scientific Document Summarization, by Haopeng Zhang and 2 other authors
View PDFAbstract: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.
| Comments: | CODI at ACL 2023 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2306.00177 [cs.CL] |
| (orarXiv:2306.00177v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2306.00177 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled Contrastive Hierarchical Discourse Graph for Scientific Document Summarization, by Haopeng Zhang and 2 other authors
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