一个无监督的基于变压器和图表的CovSumm混合文档摘要模型用于CORD-19
Akanksha Karotia1, Seba Susan1
1Department of Information Technology, Delhi Technological University, New Delhi, Delhi 110042 India.
概括
研究人员开发了CovSumm,这是一个用于总结COVID-19研究论文的新型混合模型,以应对信息过载. 这种无监督的方法显著改善了CORD-19数据集的总结性能.
科学领域:
- 自然语言处理自然语言处理.
- 人工智能的人工智能
- 生物医学信息学 生物医学信息学
背景情况:
- 由于COVID-19的流行,科学出版物数量呈指数级增长,导致研究人员和医疗专业人员的信息过载.
- 随着最新的COVID-19研究的更新至关重要,但由于大量发表的研究具有挑战性.
研究的目的:
- 通过开发一个有效的单一文档总结模型来解决COVID-19科学文献中的信息过载问题.
- 在CORD-19数据集上展示和评估一种名为CovSumm的新型混合无监督方法.
主要方法:
- 开发了一种混合文本总结模型CovSumm,它结合了两个提取方法:GenCompareSum (基于变压器) 和TextRank (基于图形).
- 该模型在CORD-19数据集中的840篇科学论文上进行了测试,特别是2021年1月1日至2021年12月31日期间发表的科学论文.
- 用于生成摘要的句子排名是基于GenCompareSum和TextRank组件的综合得分.
主要成果:
- 在CORD-19数据集上,CovSumm模型实现了最先进的性能,通过回忆导向的基底研究 (ROUGE) 的评分来衡量.
- 该模型获得的ROUGE-1分数为40.14%,ROUGE-2分数为13.25%,ROUGE-L分数为36.32%.
结论:
- 拟议的混合无监督方法CovSumm,与现有的COVID-19文献单一文档总结方法相比,表现优越.
- CovSumm提供了一个有前途的解决方案,可以有效地总结大量的科学文本,帮助研究人员了解关键发现.
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