Survey of Graph-based text summarization for HRM tasks

This study provides a comprehensive overview of graph-based algorithms and techniques for automatic text summarization, with a particular emphasis on their application in human resource management activities such as CV summarization, motivational letter summarization, and cover letter summarization. Furthermore, the paper discusses the advantages and limitations of graph-based algorithms and techniques in natural language processing based on 22 highly visible and open access articles, while also suggesting potential avenues for future research in this domain. The review highlights the importance of graph theory for automatic text summarization where it can significantly improve the quality and precision of derived information. This survey offers critical insights for researchers and practitioners in the area who seek innovative human resources text analytics approaches and techniques that helps to accelerate decision-making processes by providing informative and concise summaries of large documents.