COVID-19医疗记录提取文本摘要

Deepika S, Lakshmi Krishna N, S. S
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引用次数: 2

摘要

从原始文本文档中减少信息而保留重要信息的方法称为文本摘要。近年来,来自各种来源的可用文本数据的数量急剧增加。大量的文本是一种极好的信息来源,对来源的了解对于有效地总结有用的信息是必不可少的。摘要有助于在短时间内获取重要和所需的信息。许多领域都需要文本摘要,包括医学专业中的新闻文章摘要、电子邮件摘要和信息摘要,以跟踪患者的病史,以便将来治疗等等。在总结中,有两种方法:抽取式总结和抽象式总结。在这项工作中,对COVID-19数据集使用了提取摘要。讨论了不同的模型及其结果。
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Extractive Text Summarization for COVID-19 Medical Records
The method of reducing information from an original text document while maintaining the vital information is known as text summarizing. The amount of text data available has increased dramatically in recent years from a variety of sources. A large volume of text is an excellent source of information and knowledge of the source is essential for efficiently summarizing information that must be useful. Summarization facilitates the acquisition of vital and required information in a short period of time. Text summarization is required in a variety of domains, including news article summaries, email summaries and information summaries in the medical profession to track a patient's medical history for future treatment and so on. In summarization, there are two methods: extractive summarization and abstractive summarization. In this work, extractive summarization is used on the COVID-19 dataset. Different models and their results have been discussed.
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