Authors - Sujith Kumar Banda, Ramzan Shareef, Swathi Sowmya Bavirthi, Mohammed Arbaz Ahmed Abstract - While meetings help make company decision-making more effective, documenting and distilling the material turns out to be a lot of time-consuming work and may also contain mistakes. The project provides an automated way of transcribing audio recording of meetings into text and applying NLP for perfect creation of useful summaries. As opposed to the existing techniques that resort to either means of human beings or platform-specific ones, our solution is a versatile way that can handle transcripts from a variety of online resources. This is a system that offers both abstractive and extractive summary techniques in the form of developed transformer models, such as BERT, to form logical summaries and TF-IDF and TextRank to focus the most important points in the summary. A wider applicability of Named Entity Recognition (NER) and Part-of-Speech (POS) tagging will allow summarization over key elements, including decisions taken and responsibilities assigned. The approach aims to make the capture of output from the meeting more efficient and reliable by automatically summarizing proceedings in meetings. User input and ROUGE scores will assess how well the system performs and guarantees quality useful summaries to stakeholders.
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