论文标题
自然语言处理和文本挖掘的自动化系统文献评论：系统文献评论
Automating Systematic Literature Reviews with Natural Language Processing and Text Mining: a Systematic Literature Review
论文作者
论文摘要
目标：向SLR提出，重点是基于文本挖掘的SLR创建自动化。本评论确定了自动化研究的目标以及自动化的步骤的各个方面。在这样做时，解释了所使用的各种ML技术，进一步研究的挑战，局限性和范围。 方法：可访问的文献研究，主要集中于研究选择，研究质量评估，数据提取和SLR的数据综合部分的自动化。分析了29项研究。 结果：本综述确定了自动化研究的目标，研究选择中的步骤，研究质量评估，数据提取和数据综合部分，所使用的各种ML技术，挑战，限制和进一步研究的范围。 讨论：我们描述了NLP/TM技术的用途来支持增加系统文献评论的自动化。由于TM在SLR过程中自动化步骤的适用性，该领域在过去十年中引起了人们的关注。在数据提取，监测，质量评估和数据合成领域，TM和相关自动化技术的应用和相关自动化技术的应用存在很大差距。因此，在这一领域需要持续进展，这有望最终显着促进系统文献综述的构建。
Objectives: An SLR is presented focusing on text mining based automation of SLR creation. The present review identifies the objectives of the automation studies and the aspects of those steps that were automated. In so doing, the various ML techniques used, challenges, limitations and scope of further research are explained. Methods: Accessible published literature studies that primarily focus on automation of study selection, study quality assessment, data extraction and data synthesis portions of SLR. Twenty-nine studies were analyzed. Results: This review identifies the objectives of the automation studies, steps within the study selection, study quality assessment, data extraction and data synthesis portions that were automated, the various ML techniques used, challenges, limitations and scope of further research. Discussion: We describe uses of NLP/TM techniques to support increased automation of systematic literature reviews. This area has attracted increase attention in the last decade due to significant gaps in the applicability of TM to automate steps in the SLR process. There are significant gaps in the application of TM and related automation techniques in the areas of data extraction, monitoring, quality assessment and data synthesis. There is thus a need for continued progress in this area, and this is expected to ultimately significantly facilitate the construction of systematic literature reviews.
