# Generating Process Models by Interacting with Chatbots--A Literature Review
## 4 Conducting the Systematic Literature Review
### Data Extraction and Synthesis
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\begin{tabular}{l l} \hline \hline
**Study** & **Content** \\ \hline S1 & S1 discusses the application of NLP to automatically generate process models from existing documentation within organizations. It explores how syntactic and grammatical structures of sentences can be utilized to identify process model elements such as activities, resources, and tasks. This approach aims to streamline the process modeling phase by leveraging automated tools to interpret and convert natural language documentation into structured process models. \\ \hline S2 & S2 describes an approach that integrates NLP to facilitate the creation of process models. This technique leverages probabilistic latent semantic analysis during the preprocessing stage to manage the complexity of natural language with multiple meanings. The method aims to enhance the accuracy and efficiency of generating process models directly from textual descriptions, significantly stemming the modeling process. \\ \hline S3 & S3 explores the integration of NLP techniques with business process modeling to enhance sentiment analysis capabilities. This approach utilizes NLP to analyze textual data for sentiment and incorporates business process models to contextualize and improve the accuracy of sentiment detection. Study S3 suggests that combining these methodologies can provide more nuanced insights into sentiment data, which is particularly beneficial for applications like monitoring customer feedback and optimizing business processes. \\ \hline S4 & S4 evaluates the effectiveness of generative chatbots, like ChatTOT, in managing and executing business processes according to predefined requirements. It systematically assesses these chatbots through evaluation questions categorized into six key areas, such as Start Event, Forward Flow, and End Event, which highlight their ability to adhere to BPMN standards. The findings of Study S4 suggest that while these chatbots perform well in straightforward scenarios, difficulties emerge for tasks requiring detailed decision-making, indicating a need for further enhancements in their processing capabilities. \\ \hline S5 & S5 explores advancements in helping users generate declarative process models from natural language texts. S5 introduces the Process Highglighter, a tool that assists users in manually creating Dynamic Response Condition (DCRB) graphs, i.e., declarative process models, from textual documents. This facilitates non-technical users in adopting declarative process models, making it easier to convert complex text into structured process formats. \\ \hline S6 & S6 investigates how collaborative and mining techniques can enhance business process design. It presents a comprehensive analysis of the Ghana coco supply chain to highlight how information technology can address the asymmetry in information access among stakeholders, thereby supporting more inclusive business models. The case study details the transformation from existing business processes to more efficient and inclusive processes enabled by IT systems, aiming to improve lithological and economic output in the coco sector. \\ \hline S7 & S7 develops a method to measure how effectively chatbots can understand and execute business processes based on process mining metrics. S7 uses a large dataset from customer service interactions on Twitter (Q) to train chatbots and to evaluate their performance against normative process models. Study S7 highlights the potential of chatbots in adhering to organizational procedures, thus adding their broader adoption in practical scenarios. \\ \hline S8 & S8 explores a method for translating natural language descriptions into BPMN process models. The approach involves two main stages: analyzing textual requirements using NLP techniques to extract fact types and then using these fact types to generate BPMN diagrams. This method allows for the conversion of informal business process descriptions into formalized, visual BPMN diagrams, facilitating better understanding and communication of business processes. \\ \hline \hline \end{tabular}
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