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.

本文提出了 CHANGES，一种对比层次图神经网络，用于科学论文自动摘要。CHANGES 使用分层对话图表示科学论文，并通过专门设计的分层图信息聚合学习有效的句子表示。我们还提出了图对比学习模块，以学习全局主题感知的句子表示。在 PubMed 和 arXiv 基准数据集上的广泛实验证明了 CHANGES 的有效性以及捕捉分层结构信息在科学论文建模中的重要性。

科学文献摘要的对比分层话语图

Contrastive Hierarchical Discourse Graph for Scientific Document  Summarization

Currently, no large-scale training data is available for the task of
scientific paper summarization. In this paper, we propose a novel method that
automatically generates summaries for scientific papers, by utilizing videos of
talks at scientific conferences. We hypothesize that such talks constitute a
coherent and concise description of the papers' content, and can form the basis
for good summaries. We collected 1716 papers and their corresponding videos,
and created a dataset of paper summaries. A model trained on this dataset
achieves similar performance as models trained on a dataset of summaries
created manually. In addition, we validated the quality of our summaries by
human experts.

本文提出了一种利用科学会议上的演讲视频自动生成科技论文摘要的新方法，通过收集 1716 篇论文及其对应的视频，并创建了一个摘要数据集，最终模型在该数据集上的表现与手动摘要数据集上的模型相当，并通过人工专家验证了自动生成摘要的质量。