Paper Title
Meeting Recap Automation: Leveraging NLP and Pretrained Models for Effortless Summaries
Abstract
This research paper delves into the optimization of audio recordings conversion into textual meeting minutes,
leveraging pre-trained Natural Language Processing (NLP) models to enhance transcription accuracy and efficiency. It
introduces tokenization, a fundamental NLP technique, which dissects text into meaningful units, facilitating subsequent
analysis. The paper explores post-processing methods to refine NLP model outputs, ensuring the coherency and conciseness
of the generated meeting minutes. Additionally, the text-based meeting minutes are subjected to further summarization using
pre-trained models, offering improved accessibility and actionability for meeting participants and stakeholders. The ultimate
goal of this research is to streamline and enhance the meeting documentation process, bridging the gap between spoken
content and meaningful textual insights, thereby facilitating more effective decision-making and knowledge management.
Keywords - Speech Recognition, Voice Recording Transcription, Tokenization, Text Summarization.