Researchers continue tackling the challenge of summarizing documents too large for single API calls. Following their approach of chunking the GitLab Employee Handbook into 1360 segments, they converted text to 1272-dimensional embedding vectors. K-means clustering grouped similar content into 15 clusters, establishing the foundation for effective large document processing without losing critical context across the entire document set.

The team employed UMAP dimensionality reduction to visualize these clusters in 2D space. Each dot represents a document chunk, with colored groupings revealing semantic relationships. UMAP visualization showed clusters varying from compact to overlapping, reflecting how employee handbook topics blend policy, operations, and governance details. The silhouette score helped assess cluster quality and separation effectiveness across the 220,035 total tokens.

This method transforms raw clusters into coherent summaries by preserving thematic relationships that might be lost in traditional approaches. The technique ensures comprehensive coverage of both dominant and niche document sections. The approach provides a scalable solution for organizations needing to process massive documentation while maintaining contextual integrity across diverse subject matters.