Advancements in Semantic Resume Parsing for Autonomous Talent Acquisition: A Systematic Review

Traditional manual screening of resumes is rapidly giving way to automated recruitment systems, yet the growing body of literature on the subject remains heavily fragmented across isolated technical, managerial, and ethical perspectives. To bridge these gaps, this systematic literature review synthesizes existing research to identify best technical architectures, examine responsible artificial intelligence considerations, and outline practical design recommendations. A systematic search was conducted across multiple digital repositories. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework, 69 primary studies were ultimately retained for detailed evaluation. The findings reveal a clear evolutionary shift away from rule-based parsers towards deep learning models, transformer-based architectures, generative large language models, and hybrid systems. However, while tokenization and named entity recognition remain the most foundational document-reading mechanisms, less than one-third of the literature explicitly operationalizes ethical fairness audits or explainable artificial intelligence metrics. This review concludes that while resume parsing technologies are highly mature algorithmically, the domain itself remains methodologically and ethically underdeveloped. This immaturity stems from severe fragmentation in dataset availability, a lack of standardized output formats, and an absence of clear accountability mechanisms. Ultimately, future research must shift towards establishing open-access, multilingual benchmark datasets and embedding transparent evaluation protocols directly within production-level applicant tracking systems. Keywords— artificial intelligence; automated recruitment systems; document reading techniques; named entity recognition; resume parsing; rule-based techniques.