Contrastive Hierarchical Discourse Graph for Scientific Document Summarization

• URL: http://arxiv.org/abs/2306.00177v1
• Date: Wed, 31 May 2023 20:54:43 GMT
• Title: Contrastive Hierarchical Discourse Graph for Scientific Document Summarization
• Authors: Haopeng Zhang, Xiao Liu, Jiawei Zhang
• Abstract summary: CHANGES is a contrastive hierarchical graph neural network for extractive scientific paper summarization. We also propose a graph contrastive learning module to learn global theme-aware sentence representations.
• Score: 14.930704950433324
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

Related papers

• Enhancing Visually-Rich Document Understanding via Layout Structure Modeling [91.07963806829237]
  We propose GraphLM, a novel document understanding model that injects layout knowledge into the model. We evaluate our model on various benchmarks, including FUNSD, XFUND and CORD, and achieve state-of-the-art results.
  arXiv  Detail & Related papers  (2023-08-15T13:53:52Z)
• Scientific Paper Extractive Summarization Enhanced by Citation Graphs [50.19266650000948]
  We focus on leveraging citation graphs to improve scientific paper extractive summarization under different settings. Preliminary results demonstrate that citation graph is helpful even in a simple unsupervised framework. Motivated by this, we propose a Graph-based Supervised Summarization model (GSS) to achieve more accurate results on the task when large-scale labeled data are available.
  arXiv  Detail & Related papers  (2022-12-08T11:53:12Z)
• Multi-Document Scientific Summarization from a Knowledge Graph-Centric View [9.579482432715261]
  We present KGSum, an MDSS model centred on knowledge graphs during both the encoding and decoding process. Specifically, in the encoding process, two graph-based modules are proposed to incorporate knowledge graph information into paper encoding. In the decoding process, we propose a two-stage decoder by first generating knowledge graph information of summary in the form of descriptive sentences, followed by generating the final summary.
  arXiv  Detail & Related papers  (2022-09-09T14:20:59Z)
• An unsupervised cluster-level based method for learning node representations of heterogeneous graphs in scientific papers [16.019656763017004]
  This paper proposes an unsupervised cluster-level scientific paper heterogeneous graph node representation learning method (UCHL) Based on the heterogeneous graph representation, this paper performs link prediction on the entire heterogeneous graph and obtains the relationship between the edges of the nodes, that is, the relationship between papers and papers.
  arXiv  Detail & Related papers  (2022-03-31T02:13:39Z)
• CitationIE: Leveraging the Citation Graph for Scientific Information Extraction [89.33938657493765]
  We use the citation graph of referential links between citing and cited papers. We observe a sizable improvement in end-to-end information extraction over the state-of-the-art.
  arXiv  Detail & Related papers  (2021-06-03T03:00:12Z)
• BASS: Boosting Abstractive Summarization with Unified Semantic Graph [49.48925904426591]
  BASS is a framework for Boosting Abstractive Summarization based on a unified Semantic graph. A graph-based encoder-decoder model is proposed to improve both the document representation and summary generation process. Empirical results show that the proposed architecture brings substantial improvements for both long-document and multi-document summarization tasks.
  arXiv  Detail & Related papers  (2021-05-25T16:20:48Z)
• Enhancing Scientific Papers Summarization with Citation Graph [78.65955304229863]
  We redefine the task of scientific papers summarization by utilizing their citation graph. We construct a novel scientific papers summarization dataset Semantic Scholar Network (SSN) which contains 141K research papers in different domains. Our model can achieve competitive performance when compared with the pretrained models.
  arXiv  Detail & Related papers  (2021-04-07T11:13:35Z)
• SUGAR: Subgraph Neural Network with Reinforcement Pooling and Self-Supervised Mutual Information Mechanism [33.135006052347194]
  This paper presents a novel hierarchical subgraph-level selection and embedding based graph neural network for graph classification, namely SUGAR. SUGAR reconstructs a sketched graph by extracting striking subgraphs as the representative part of the original graph to reveal subgraph-level patterns. To differentiate subgraph representations among graphs, we present a self-supervised mutual information mechanism to encourage subgraph embedding.
  arXiv  Detail & Related papers  (2021-01-20T15:06:16Z)
• Hierarchical Graph Capsule Network [78.4325268572233]
  We propose hierarchical graph capsule network (HGCN) that can jointly learn node embeddings and extract graph hierarchies. To learn the hierarchical representation, HGCN characterizes the part-whole relationship between lower-level capsules (part) and higher-level capsules (whole)
  arXiv  Detail & Related papers  (2020-12-16T04:13:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.