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Related Experiment Video

Updated: Oct 5, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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COVIDSum: A linguistically enriched SciBERT-based summarization model for COVID-19 scientific papers.

Xiaoyan Cai1, Sen Liu1, Libin Yang1

  • 1School of Automation, Northwestern Polytechnic University, Xi'an 710072, Shaanxi, People's Republic of China.

Journal of Biomedical Informatics
|February 1, 2022
PubMed
Summary

Researchers developed COVIDSum, a new abstractive summarization model using SciBERT and graph networks. This tool helps automate abstract generation for COVID-19 scientific papers, reducing researcher workload.

Keywords:
Abstractive summarizationCOVID-19 scientific papersLinguistically enriched pre-trained language modelSciBERT

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Area of Science:

  • Computational linguistics
  • Biomedical informatics
  • Artificial intelligence

Background:

  • The COVID-19 pandemic has spurred extensive research, leading to a surge in scientific publications.
  • Manually creating abstracts for these papers is time-consuming and increases researchers' workload.
  • Automated abstractive summarization offers a potential solution to streamline the dissemination of research findings.

Purpose of the Study:

  • To develop an automated abstractive summarization model specifically for COVID-19 scientific papers.
  • To reduce the burden on researchers by providing reliable draft abstracts.
  • To improve the efficiency of scientific knowledge sharing in the context of the pandemic.

Main Methods:

  • The proposed model, COVIDSum, utilizes a linguistically enriched SciBERT-based approach.
  • It involves extracting salient sentences and constructing word co-occurrence graphs from source papers.
  • A SciBERT-based sequence encoder and a Graph Attention Networks-based graph encoder are employed, with their outputs fused for summary generation.

Main Results:

  • COVIDSum demonstrated significant performance improvements over existing document summarization models.
  • The model was evaluated on the publicly available COVID-19 open research dataset.
  • The abstractive summaries generated by COVIDSum were found to be reliable and accurate.

Conclusions:

  • The developed COVIDSum model effectively addresses the challenge of abstract generation for COVID-19 research papers.
  • This approach can significantly alleviate the writing burden for researchers in this critical field.
  • Automated summarization holds promise for accelerating scientific communication during global health crises.