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08. Real-Time Meeting Summariser

Authors
Affiliations
Birmingham City University
Sunway College Kathmandu

Objective

Transcribes, summarizes, and extracts action items from live meetings in real-time, combining speech-to-text, NLP, and meeting understanding.

System Architecture

[Mermaid diagram - flowchart showing core components and data flow]

[3-5 sentence description of architecture]

Technical Approach

Key Components

Pipeline / Data Flow

[Detailed description of request → processing → response flow]

Complexity Analysis

MetricComplexityNotes
Model sizeASR: 100M-500M, Summarizer: 3B-13B[implications]
Time complexityO(audio_length)[notes]
Space complexity~2-10GB streaming[notes]
Latency target<2s delay from speech[real-time vs. batch]
Throughput target100-1000 concurrent streams[per GPU/instance]

Pros & Cons

Pros

Cons

Trade-offs

[1-2 paragraphs discussing key technical trade-offs]

Real-World Applications

Where This Pattern Appears

Production Considerations

[2-3 paragraphs on scaling, failure modes, monitoring, cost]

References & Citations

Citation 1: Architecture & Design

Title: [Paper/Blog Title on Real-Time Meeting Summariser Architecture]

Citation 2: Performance & Benchmarks

Title: [Performance Benchmarks for Real-Time Meeting Summariser]

Citation 3: Implementation Details

Title: [Implementation Details and Trade-offs]

Citation 4: Real-World Deployment

Title: [Production Deployment Insights]

Reproducibility Checklist