mlsanityData to 14 Oct 2026

Contrastive Hierarchical Discourse Graph for Scientific Document Summarization

arXiv 2306.00177 ↗First posted 31 May 2023cs.CLcited by 9

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Abstract (arXiv)

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.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 38 of its 51 references that are in the map. First posted on arXiv 31 May 2023.

Builds on

38 of 51 references.

References · 38

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26 more
  1. 2019Text Summarization with Pretrained Encoderscited by 556
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  5. 2020Extractive Summarization as Text Matchingcited by 189
  6. 2021SimCLS: A Simple Framework for Contrastive Learning of Abstractive Summarizationcited by 132
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  11. 2019Sentence Centrality Revisited for Unsupervised Summarizationcited by 65
  12. 2019Extractive Summarization of Long Documents by Combining Global and Local Contextcited by 59
  13. 2023Extractive Summarization via ChatGPT for Faithful Summary Generationcited by 59
  14. 2018Neural Document Summarization by Jointly Learning to Score and Select Sentencescited by 56
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  19. 2022HiStruct+: Improving Extractive Text Summarization with Hierarchical Structure Informationcited by 26
  20. 2023DiffuSum: Generation Enhanced Extractive Summarization with Diffusioncited by 25
  21. 2020Systematically Exploring Redundancy Reduction in Summarizing Long Documentscited by 21
  22. 2022Improving the Faithfulness of Abstractive Summarization via Entity Coverage Controlcited by 21
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 1; 2024: 5; 2025: 1; 2026: 2.

9 papers.

  1. 2024A Systematic Survey of Text Summarization: From Statistical Methods to Large Language Modelscited by 52
  2. 2026What Should I Cite? A RAG Benchmark for Academic Citation Predictioncited by 5
  3. 2024Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Levelcited by 4
  4. 2024DomainSum: A Hierarchical Benchmark for Fine-Grained Domain Shift in Abstractive Text Summarizationcited by 4
  5. 2024GraphLSS: Integrating Lexical, Structural, and Semantic Features for Long Document Extractive Summarizationcited by 4
  6. 2024Prompt-tuning for Clickbait Detection via Text Summarizationcited by 3
  7. 2023Unsupervised Multi-document Summarization with Holistic Inferencecited by 2
  8. 2025TreeReader: A Hierarchical Academic Paper Reader Powered by Language Modelscited by 1
  9. 2026LECTOR: Joint Optimization of Scientific Reasoning Graphs and Introduction Generationcited by 0