# Fully Open-Source Meeting Minutes Generation Tool
## 1 Introduction  
The shift to online work has made video conferencing essential, leading to a significant increase in online presentations at conferences and meetings. However, the excessive use of online meetings has increased fatigue among participants [1, 2]. It is even common practice now to record online meetings for later review, as participants struggle to process all the information during live sessions. Recent advances in _artificial intelligence (AI)_ offer solutions by helping users to retrieve key information efficiently from recorded meetings, reducing the need for time-consuming reviews [3]. _AI_ can capture key information from recorded meetings using audio, visual, and/or text data by applying _information extraction (IE)_ methods [4]. The _IE_ methods convert unstructured data into structured formats in order to highlight relevant information. However, the existing proprietary systems limit flexibility and adaptability in implementing new models and restrict accessibility for key information retrieval, while an open-source approach offers more flexibility in adapting the latest models to achieve the best results. Integrating multiple models and methods into one system requires careful selection. Therefore,in this paper, we investigate whether fully open-source models can be effectively integrated and implemented for the generation of meeting minutes in the context of recorded online meetings. For this purpose, suitable open-source models for the system need to be identified. Then, the integration of the selected models needs to be conducted in order to develop a modular and adaptable system. Finally, the developed system needs to be compared with existing proprietary systems. The use of open-source models and methods is driven by their wide accessibility, cost effectiveness, and the benefits of continuous community contributions. Coupled with the rapid advancements in _natural language processing (NLP)_, this approach offers the ability to quickly adopt the latest innovations. Thus, it enhances the system performance and improves the quality of generated meeting minutes. This is highly advantageous and beneficial for individuals or organizations in need of such a system. In this paper, an integrated and modular fully open-source system is proposed by combining multiple methods of video analysis and information extraction. Since online presentations are often comprised of multiple slides, _scene-change detection_ is applied in order to segment the video based on the slide changes. Then, since the speaker's speech also contains valuable information, an _audio-to-text_ method is employed to capture this content. Since the resulting text data can be extensive, _summarization_ and _keyword extraction_ techniques are used to quickly convey the main points of the presentation. Considering that the video and textual information are processed separately, inaccurate matching could cause confusion for readers. To address this, an information correlation mechanism using _OCR_ and _regular expressions (regex)_ are applied. The proposed system was evaluated using ten presentation videos that were created under controlled conditions, although they may not fully reflect the complexity of real-world online presentations. The system's performance was measured in terms of accuracy and efficiency, using statistical metrics appropriate for each respective method. Additionally, user feedback was collected through a questionnaire, focusing on the quality of transcriptions, summaries, keyword extraction, and the intuitiveness of the user interface. Building upon the explanation of the proposed system, namely a fully open-source model for generating meeting minutes, the contributions of the proposal can be summarized into the following points:  
* Designing a meeting minutes generation system by combing several methods that have been implemented by open-source programs.