The development of a tool based on Natural Language Processing (NLP) models is presented. The presented tool is an improvement on the original virtual human factors classificator developed to assist experts with extracting the organizational, technological, and individual factors that may trigger human errors. To identify the performance shaping factors, the approach proposed is based on classifying text according to previously labelled accident reports by human experts. Making use of BERT (Bidirectional Encoder Representations from Transformers), a popular transformer-based machine learning model for NLP. In addition, a method to provide a summarization of each accident report is presented. This provides further detailed context alongside with the identified performance shaping factors, without the need of reading the entire report which is generally a significant task. The tool performs abstractive summarization as it aims to understand the entire report and generate paraphrased text to summarize the main points. In this work, BART (Bidirectional and Auto-Regressive Transformers), which is a denoising autoencoder for pre-training sequence-to-sequence models, has been used as the basis for the text summarization model.