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App. Res. of Comp. Accepted Paper
Vol. 42, 2025 No. 3

Integrating contrastive learning and dual-stream networks for knowledge graph summarization models

Zhao Xia
Wang Zhao
Hebei University of Economics & Business School of Management Sciences & Information Engineering, Shijiazhuang 050061, China
DOI: 10.19734/j.issn.1001-3695.2024.07.0304

Abstract

This study presents a novel knowledge graph-based summarization model (KGDR-CLSUM) , which integrates contrastive learning with a dual-stream network to address factual errors and improve information extraction in existing summarization models. The model uses a dual-stream network to process textual and knowledge graph features simultaneously, while contrastive learning enhances the integration of these features. Additionally, we introduce a momentum distillation strategy to reduce data noise in the knowledge graph, improving the quality and accuracy of the generated summaries. On the CNN/Daily Mail dataset, KGDR-CLSUM outperforms the baseline model PEGASUSBASE, improving ROUGE-1, ROUGE-2, and ROUGE-L scores by 3.03%, 3.42%, and 2.56%, respectively. On the XSum dataset, we observe even more significant improvements of 7.54%, 8.78%, and 8.51%. Human evaluations also report significantly higher scores compared to ChatGPT, further demonstrating the superior performance of our model. These results show that KGDR-CLSUM effectively minimizes factual errors and significantly enhances summary quality, especially for short-text generation tasks.

Foundation Support

河北省自然科学基金:"云-边-端"协同下任务智能调度方法研究及应用(F2021207005)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.07.0304
Publish at: Application Research of Computers Accepted Paper, Vol. 42, 2025 No. 3

Publish History

[2024-12-11] Accepted Paper

Cite This Article

赵霞, 王钊. 结合对比学习和双流网络融合知识图谱摘要模型 [J]. 计算机应用研究, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0304. (Zhao Xia, Wang Zhao. Integrating contrastive learning and dual-stream networks for knowledge graph summarization models [J]. Application Research of Computers, 2025, 42 (3). (2024-12-16). https://doi.org/10.19734/j.issn.1001-3695.2024.07.0304. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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