Meetings are pivotal for decision-making, project coordination, and collaboration within organizations. However, capturing key takeaways from these discussions often poses challenges, especially in remote settings. The traditional manual summarization of meetings can be inefficient, leading to omissions or misinterpretations. Fortunately, advancements in large language models (LLMs) offer a transformative approach by converting unstructured meeting transcripts into structured summaries and action items. This advancement is particularly beneficial for domains like project management, customer support, sales, legal, and enterprise knowledge management.
Leveraging LLMs for Meeting Insights
Modern LLMs excel in summarization and action item extraction due to their contextual understanding and the ability to generate structured outputs. This is achieved through prompt engineering, which offers a scalable alternative to traditional model fine-tuning. Instead of altering the model architecture or relying on extensive labeled datasets, prompt engineering uses carefully crafted input queries to guide the model’s behavior, influencing the output’s content and format. This method allows for rapid, domain-specific customization, making it ideal for dynamic environments where model behaviors need swift adjustments without resource-intensive retraining.
Amazon Nova Models and Amazon Bedrock
Unveiled at AWS re:Invent in December 2024, Amazon Nova models deliver cutting-edge intelligence with industry-leading price performance. These models are optimized to power enterprise generative AI applications securely and cost-effectively. The Nova family consists of four tiers:
– Nova Micro : Text-only, ultra-efficient for edge use.
– Nova Lite : Multimodal, balanced for versatility.
– Nova Pro : Multimodal, balancing speed and intelligence, ideal for most enterprise needs.
– Nova Premier : Multimodal, the most capable model for complex tasks and serves as a teacher for model distillation.
With Amazon Bedrock Model Distillation, users can bring Nova Premier’s intelligence to faster, more cost-effective models like Nova Pro or Nova Lite. This is facilitated through the Amazon Bedrock console and APIs such as Converse API and Invoke API.
Solution Overview
This solution utilizes Amazon Nova understanding models via Amazon Bedrock for automated insight extraction using prompt engineering. Two key outputs are focused on:
– Meeting Summarization : High-level abstractive summaries that capture key discussion points, decisions, and updates.
– Action Items : Structured lists of actionable tasks derived from the meeting conversation.
Prerequisites and Solution Components
To implement this solution, familiarity with calling LLMs using Amazon Bedrock is recommended. The solution comprises two core features—meeting summarization and action item extraction—utilizing popular models available through Amazon Bedrock. Specific prompts were used for these tasks:
– For meeting summarization, a one-shot approach with persona assignment was employed to ensure consistent format and adherence to rules regarding tone, style, length, and faithfulness to the transcript.
– For action items, specific prompt instructions and chain-of-thought approaches improved the quality of generated content, avoiding redundancy.
Evaluation Framework and Results
Evaluating LLM outputs in meeting summarization and action item extraction is complex. Traditional metrics like ROUGE, BLEU, and METEOR are limited in capturing nuances such as factual correctness and coherence. LLM-as-a-judge, where another LLM assesses output quality, offers a scalable solution. Using Anthropic’s Claude 3.5 Sonnet v1, outputs were scored on faithfulness, summarization, and QA metrics.
In summarization, Nova Premier achieved the highest faithfulness score (1.0) with a processing time of 5.34s, while Nova Pro secured a 0.94 score in 2.9s. Nova Lite and Nova Micro provided faster processing times but lower faithfulness scores. For action item extraction, Nova Premier led with 0.83 faithfulness and a 4.94s processing time, followed by Nova Pro. Interestingly, Nova Micro outperformed Nova Lite in this task despite its smaller size. These findings highlight performance-speed dynamics across the Amazon Nova model family for text-processing applications.
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Note: This article is inspired by content from https://aws.amazon.com/blogs/machine-learning/meeting-summarization-and-action-item-extraction-with-amazon-nova/. It has been rephrased for originality. Images are credited to the original source.