Meetings have long been a pillar in the business world, essential for decision-making and project coordination. However, the task of summarizing the key points of these conversations can be laborious and prone to errors. Currently, technology offers more efficient solutions, using large language models (LLMs) to transform scattered transcripts into organized summaries and action items.
Amazon has introduced to the market its family of Nova models, available through Amazon Bedrock, to optimize the summarization of meetings. These LLMs are especially effective at understanding complex contexts and generating structured outputs, facilitating project management, sales, and customer service, among others.
The prompt engineering process stands out as an innovative alternative to traditional model fine-tuning. Rather than modifying or retraining a model, this technique uses crafted queries to adjust the model's behavior in an efficient and domain-specific way.
The Amazon Nova models, presented at AWS re:Invent in 2024, include four levels: Nova Micro, Nova Lite, Nova Pro, and Nova Premier. These are optimized for generative applications in secure environments, standing out for their performance and affordable cost. The solution concentrates on summarizing meetings and extracting actions, generating summaries that distill discussions and decisions, and clear task lists.
To evaluate its efficacy, data from the QMSum dataset were used, including meeting transcripts and manual summaries. The results reveal that Nova Premier offers the highest fidelity, although other models also show competitive processing times.
Evaluating the quality of the results generated by LLMs is usually challenging. Traditional metrics do not always reflect factual accuracy or coherence. To address this, an LLM has begun to be implemented as a judge to evaluate outputs based on clear criteria.
The patterns observed in the Amazon Nova models show a clear relationship between performance and latency, making this family of models an attractive option for companies that handle large volumes of meeting data.
In summary, the combination of prompt engineering with Amazon Nova represents an effective solution for meeting management. Its implementation not only optimizes latency and costs, but also significantly improves the accuracy of automated summaries, providing companies with a valuable tool for knowledge management.
