Meeting summarization and action item extraction with Amazon Nova

Meetings play a crucial role in decision-making, project coordination, and collaboration, and remote meetings are common across many organizations. However, capturing and structuring key takeaways from these conversations is often inefficient and inconsistent. Manually summarizing meetings or extracting action items requires significant effort and is prone to omissions or misinterpretations.

Large language models (LLMs) offer a more robust solution by transforming unstructured meeting transcripts into structured summaries and action items. This capability is especially useful for project management, customer support and sales calls, legal and compliance, and enterprise knowledge management.

In this post, we present a benchmark of different understanding models from the Amazon Nova family available on Amazon Bedrock, to provide insights on how you can choose the best model for a meeting summarization task.

LLMs to generate meeting insights

Modern LLMs are highly effective for summarization and action item extraction due to their ability to understand context, infer ...