Organizations increasingly rely on vast amounts of unstructured textual data to describe and manage their business processes. Recent advances in Natural Language Processing (NLP) and Large Language Model (LLM)s, demonstrate significant potential for automating the extraction of process information and generating Business Process Model and Notation (BPMN) models directly from text. However, a comprehensive assessment of the methods, tools, and evaluation outcomes related to this field is missing. We present a Systematic Literature Review (SLR) that synthesizes current research on NLP-based BPMN model generation. We show which NLP approaches have been used to extract process information before and after LLMs. Analyzing 17 primary studies, we extracted information on intermediate representations of models, on supported BPMN elements, generation methods, file types, and visualizations. We also reviewed how the selected publications evaluated model generation, and derived strengths, limitations, and future directions of the field. We found that integrating rule-based or LLM feedback loops could refine model generation accuracy, and establishing benchmarks and open datasets would increase reproducibility.