Efficient Meeting Insights: NLP-Enhanced Summarization of Voice and Text

In order to improve meeting efficiency in the ever-changing workplace, research has mostly focused on sophisticated text summarization techniques such as machine learning, hybrid, semantic, and graph-based methods. These approaches have shown efficient in extracting key information from lengthy talks, hence dramatically improving decision-making and knowledge retention. However, their potential has been somewhat limited by a major concentration on processing material in a single language, which ignores the complex, multilingual environment of modern global enterprises. Our most recent research project tries to overcome this gap by combining these various summarizing approaches into acomplete, language-neutral framework.Furthermore, to address the specific difficulty of long meetings, when extracting insights might be especially difficult for those who are not there, we propose using the Longformer encoder-decoder paradigm. This methodology excels at distilling lengthy talks into digestible summaries, regardless of the original language, ensuring that critical insights are easily accessible to anybody, hence increasing inclusion and comprehension across multinational teams.