Automatic business process discovery from textual process documentation is highly desirable to reduce the time and cost of Business Process Management (BPM) implementation in organizations. However, existing automatic process discovery approaches mainly focus on identifying activities out of the documentations. Deriving the hierarchical structural relationships between activities, which is important in the whole process discovery scope, requires great human labeling effort and is still a challenge. Facing this challenge, we propose to retrieve the latent hierarchical structure present in the textual process documentation by building a neural network without any extra human-labeled knowledge. The proposed neural network leverages a novel recurrent architecture, Ordered Neurons LSTM (ON-LSTM), with a process-level language model objective. On the base of this, we provide an automatic business process service (A-BPS) which could support the goal of automatically discovering business processes out of the uploaded documentation and generate Business Process Model and Notation (BPMN) scripts to represent the discovered process model through a service ecosystem. Experimental results show that 58.76% of the hierarchical structures could be correctly retrieved. A-BPS could generate 32% of the whole BPMN process model with average execution time 5-8 minutes. The time benefit of A-BPS is up to 40% reduction.