Abstract

Objective

Biomedical text summarization helps biomedical information seekers avoid information overload by reducing the length of a document while preserving the contents’ essence. Our systematic review investigates the most recent biomedical text summarization researches on biomedical literature and electronic health records by analyzing their techniques, areas of application, and evaluation methods. We identify gaps and propose potential directions for future research.

Materials and Methods

This review followed the PRISMA methodology and replicated the approaches adopted by the previous systematic review published on the same topic. We searched 4 databases (PubMed, ACM Digital Library, Scopus, and Web of Science) from January 1, 2013 to April 8, 2021. Two reviewers independently screened title, abstract, and full-text for all retrieved articles. The conflicts were resolved by the third reviewer. The data extraction of the included articles was in 5 dimensions: input, purpose, output, method, and evaluation.

Results

Fifty-eight out of 7235 retrieved articles met the inclusion criteria. Thirty-nine systems used single-document biomedical research literature as their input, 17 systems were explicitly designed for clinical support, 47 systems generated extractive summaries, and 53 systems adopted hybrid methods combining computational linguistics, machine learning, and statistical approaches. As for the assessment, 51 studies conducted an intrinsic evaluation using predefined metrics.

Discussion and Conclusion

This study found that current biomedical text summarization systems have achieved good performance using hybrid methods. Studies on electronic health records summarization have been increasing compared to a previous survey. However, the majority of the works still focus on summarizing literature.

中文翻译：

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生物医学文献和 EHR 自动文本摘要的系统评价

摘要

客观的

生物医学文本摘要通过在保留内容本质的同时减少文档长度来帮助生物医学信息搜索者避免信息过载。我们的系统综述通过分析生物医学文献和电子健康记录的技术、应用领域和评估方法，调查了最近关于生物医学文献和电子健康记录的生物医学文本摘要研究。我们找出差距并提出未来研究的潜在方向。

材料和方法

本综述遵循 PRISMA 方法，并复制了之前发表在同一主题上的系统综述所采用的方法。我们从 2013 年 1 月 1 日至 2021 年 4 月 8 日检索了 4 个数据库（PubMed、ACM 数字图书馆、Scopus 和 Web of Science）。两名审稿人独立筛选了所有检索到的文章的标题、摘要和全文。冲突由第三位审稿人解决。纳入文章的数据提取为5个维度：输入、目的、输出、方法和评价。

结果

7235 篇检索到的文章中有 58 篇符合纳入标准。39 个系统使用单文档生物医学研究文献作为输入，17 个系统明确设计用于临床支持，47 个系统生成提取摘要，53 个系统采用结合计算语言学、机器学习和统计方法的混合方法。至于评估，51 项研究使用预定义的指标进行了内在评估。

讨论与结论

本研究发现，当前的生物医学文本摘要系统使用混合方法取得了良好的性能。与之前的调查相比，对电子健康记录摘要的研究一直在增加。然而，大部分作品仍然侧重于对文学的总结。