用机器学习提升社科领域系统性综述的效率与范围。
Machine Learning Information Retrieval and Summarisation to Support Systematic Review on Outcomes Based Contracting
- 结合专家指导的信息检索与摘要技术自动化文献筛选。
- 显著减少人工标注时间，提升综述流程可扩展性。
- 适合需要高效处理大量文献的研究者或政策制定者。
随着学术文献激增，传统综述方法面临文献量大、类型多样带来的挑战。本文提出利用先进的机器学习（ML）和自然语言处理（NLP）工具，提升社会科学领域系统性综述的效率与覆盖范围。重点聚焦于自动化重复性强、耗时长的人工标注环节，通过信息检索与摘要技术实现快速扩展。研究基于专家反馈优化模型，验证了该方法在提高综述速度与规模上的可行性。文章总结了集成式综述流程的经验，并指出未来需加强模型可解释性以提升可信度。
原文摘要 · Abstract (English)
As academic literature proliferates, traditional review methods are increasingly challenged by the sheer volume and diversity of available research. This article presents a study that aims to address these challenges by enhancing the efficiency and scope of systematic reviews in the social sciences through advanced machine learning (ML) and natural language processing (NLP) tools. In particular, we focus on automating stages within the systematic reviewing process that are time-intensive and repetitive for human annotators and which lend themselves to immediate scalability through tools such as information retrieval and summarisation guided by expert advice. The article concludes with a summary of lessons learnt regarding the integrated approach towards systematic reviews and future directions for improvement, including explainability.
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