How to summarize meetings with LLMs

The remote-first world is creating a massive efficiency gap in team operations and collaboration due to the loss of crucial information during virtual meetings. AssemblyAI's LeMUR framework can automatically capture and analyze these meetings, generating structured summaries, clear action items, and actionable insights using large language models. To get started, an AssemblyAI API key must be obtained and installed Python SDK for calling the API from Python code. The code then runs speech-to-text on a file of choice, analyzes the transcript, and generates a meeting summary based on a prompt. LeMUR supports various LLMs, including Claude 3 Sonnet, which outperforms other models in evaluations. Customizing summaries is also possible, allowing users to tailor formats to suit their needs. High-quality audio input and well-structured meetings are essential for improving the quality of automated analysis. The best practices include using consistent meeting protocols, implementing a clear agenda, and designating specific times for questions and discussion.

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