This paper introduces an algorithmic framework for conducting systematic literature reviews (SLRs), designed to improve efficiency, reproducibility, and selection quality assessment in the literature review process. The proposed method integrates Natural Language Processing (NLP) techniques, clustering algorithms, and interpretability tools to automate and structure the selection and analysis of academic publications. The framework is applied to a case study focused on financial narratives, an emerging area in financial economics that examines how structured accounts of economic events, formed by the convergence of individual interpretations, influence market dynamics and asset prices. Drawing from the Scopus database of peer-reviewed literature, the review highlights research efforts to model financial narratives using various NLP techniques. Results reveal that while advances have been made, the conceptualization of financial narratives remains fragmented, often reduced to sentiment analysis, topic modeling, or their combination, without a unified theoretical framework. The findings underscore the value of more rigorous and dynamic narrative modeling approaches and demonstrate the effectiveness of the proposed algorithmic SLR methodology.

翻译：本文提出了一种用于开展系统化文献综述的算法框架，旨在提升文献综述过程的效率、可重复性及选择质量评估。该方法整合了自然语言处理技术、聚类算法与可解释性工具，以实现学术文献筛选与分析过程的自动化与结构化。该框架被应用于一个聚焦金融叙事的案例研究——金融经济学中一个新兴领域，致力于探究由个体解读汇聚形成的、关于经济事件的结构化叙述如何影响市场动态与资产价格。基于Scopus同行评议文献数据库，本综述重点梳理了运用多种NLP技术对金融叙事进行建模的研究成果。结果表明，尽管该领域已取得进展，但金融叙事的概念构建仍呈碎片化，常被简化为情感分析、主题建模或其组合，缺乏统一的理论框架。这些发现凸显了采用更严谨、动态的叙事建模方法的价值，并验证了所提出的算法化系统文献综述方法的有效性。