Public Consultation Summarisation
The Challenge
Public consultations on reform proposals generate thousands of written submissions from citizens, organisations, and stakeholders. The Ministry for Equality & Reforms needed to process, analyse, and summarise this large volume of feedback quickly and objectively. Manual review was not only slow but also risked inconsistent interpretation of citizen input, potentially overlooking important themes or minority viewpoints.
Our Approach
Neural AI developed an AI-driven summarisation pipeline tailored to public consultation data:
- Automated ingestion and preprocessing of consultation submissions across multiple formats including online forms, emails, and PDF documents
- NLP-powered thematic analysis identifying recurring themes, concerns, and recommendations across the full corpus of responses
- Sentiment analysis detecting the tone and intensity of public opinion on specific reform elements
- Hierarchical summarisation producing both high-level executive summaries and detailed topic-by-topic breakdowns for different stakeholder needs
- Bias-aware processing designed to give equal weight to all submissions regardless of length or complexity, ensuring representative analysis
"AI-powered summarisation transforms how governments process public feedback, ensuring every citizen's voice is heard and analysed without delay."
Key Results
- Thousands of consultation responses summarised in hours instead of weeks
- Consistent, unbiased thematic analysis across all submissions
- Key themes and sentiment patterns identified automatically
- Reduced manual review workload by over 85%
- Actionable summary reports generated for policymakers
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