Business Process Modeling Languages (BPML’s) are continuously getting attraction of software development communities due to the fact of specifying complex business requirements with simplicity. However, the development of business process models from textual requirements through existing BPML’s is a time consuming task. In this context, Natural Language Processing (NLP) techniques are commonly applied to automatically generate business process models from textual requirements. Business Process Model and Notation (BPMN) is a well-known BPML. This article comprehensively investigates modern techniques, tools and trends for the generation of BPMN models from textual requirements by utilizing NLP techniques. Particularly, a Systematic Literature Review (SLR) is performed to select and evaluate 36 research studies published in the span of 2010–2018. As a result, 11 NLP and 8 BPMN tools are identified. Furthermore, 8 commonly generated BPMN constructs are recognized. Finally, a comparative analysis of NLP and BPMN tools is performed with the help of important evaluation parameters. It is concluded that the existing NLP techniques and tools significantly simplify the process of BPMN models generation from textual requirements. However, the existing approaches are inadequate to be applied in the industries, especially for real-time systems.