• Ardon Kotey
• Riya Deorukhkar
• Chahat Kothari
• Sweedle Machado

In various professional contexts, such as meetings, interviews, or discussions, efficient note-taking stands as a crucial necessity for capturing vital points, decisions, and action items. Traditionally, participants in these conversations are typically required to transcribe and summarize the content manually after the fact. While these conventional note-taking methods offer a degree of effectiveness, they often prove to be time-consuming, challenging, and prone to human error, particularly when dealing with lengthy or intricate conversations. However, this cumbersome process can be significantly streamlined and improved through the automation capabilities of advanced technologies such as Automatic Speech Recognition (ASR), Machine Learning (ML) and Natural Language Processing (NLP). These tools can be harnessed to create automated systems that analyze spoken content in real-time, allowing for the instant extraction of key information and the generation of concise summaries. This innovative approach not only reduces the burden of manual notetaking but also enhances the accuracy and efficiency of the summarization process. ASR, precisely converting audio to text, identifies speakers for better context. This, coupled with ML algorithms that identify significant topics and insights as conversations unfold, improves context awareness. NLP techniques further amplify capabilities, recognizing sentence relationships and performing sentiment analysis for a comprehensive understanding of conversational nuances. Through comprehensive evaluation against conventional manual methods using a diverse dataset, this framework demonstrates superior efficiency, accuracy, scalability, and noteworthy improvements in summarization quality and processing speed.

ASR, NLP, ML, summarization, note-taking.

"INTEGRATED ASR, NLP, AND ML FRAMEWORK FOR CONVERSATIONAL SUMMARIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 11, page no.d666-d673, November-2023, Available :http://www.jetir.org/papers/JETIR2311382.pdf

"INTEGRATED ASR, NLP, AND ML FRAMEWORK FOR CONVERSATIONAL SUMMARIZATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 11, page no. ppd666-d673, November-2023, Available at : http://www.jetir.org/papers/JETIR2311382.pdf