By consolidating scattered knowledge, the literature review provides a comprehensive understanding of the investigated topic. However, reading, conducting, or peer-reviewing review papers generally demands a significant investment of time and effort from researchers. To improve efficiency, this paper aims to provide a thorough review of reviews in the PAMI field from diverse perspectives. First, this paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews. To facilitate this, a meta-data database dubbed RiPAMI, and a topic dataset are constructed. Second, based on these indicators, the study presents comparative analyses of representative reviews, unveiling the characteristics of publications across various fields, periods, and journals. The newly emerging AI-generated literature reviews are also appraised, and the observed differences suggest that most AI-generated reviews still lag behind human-authored reviews in multiple aspects. Third, we briefly provide a subjective evaluation of representative PAMI reviews and introduce a paper structure-based typology of literature reviews. This typology may improve the clarity and effectiveness for scholars in reading and writing reviews, while also serving as a guide for AI systems in generating well-organized reviews. Finally, this work offers insights into the current challenges of literature reviews and envisions future directions for their development.

翻译：通过整合分散的知识，文献综述为所研究主题提供了全面理解。然而，阅读、撰写或同行评议综述论文通常需要研究人员投入大量时间和精力。为提高效率，本文旨在从多角度对PAMI领域的综述进行系统评述。首先，本文提出多项文章级、领域归一化及大语言模型赋能的文献计量指标用于评价综述。为此，构建了名为RiPAMI的元数据库与主题数据集。其次，基于这些指标，研究对代表性综述进行对比分析，揭示了不同领域、时期及期刊的论文特征。同时评估了新兴的AI生成文献综述，观察到的差异表明多数AI生成综述在多个维度仍落后于人工撰写综述。第三，我们简要提供对代表性PAMI综述的主观评价，并引入基于论文结构的文献综述类型学。该类型学可提升学者阅读与撰写综述的清晰度与效率，同时为AI系统生成结构合理的综述提供指导。最后，本文剖析了当前文献综述面临的挑战，并展望其未来发展方向。