Our solution implements a sophisticated multi-stage pipeline that transforms raw PDF documents into persona-specific insights through advanced relevance scoring and intelligent content extraction.
We enhanced the robust Round 1A PDF extraction to create structured sections rather than just outlines. The system intelligently merges text spans into logical lines, detects heading hierarchies, and extracts content boundaries. For image-based PDFs, we employ OCR with multilingual support. Each document is decomposed into semantically meaningful sections with preserved page references and hierarchical context.
The persona analyzer employs pattern recognition to classify users into eight archetypes (researcher, analyst, student, etc.) based on linguistic cues and domain indicators. It extracts expertise areas, assesses experience levels, and identifies domain focus through keyword analysis. This creates a rich persona profile that guides subsequent relevance computations.
We parse job descriptions to identify primary actions (analyze, summarize, compare, etc.), extract key terms, and determine content preferences (quantitative vs qualitative, methodological vs practical). Priority indicators and specific requirements are extracted using regex patterns and linguistic analysis to understand what the user truly needs.
Our scoring engine combines five weighted dimensions:
- Semantic Similarity (35%): TF-IDF-inspired approach matching section content to persona keywords
- Keyword Overlap (25%): Jaccard similarity between section vocabulary and target terms
- Content Type Match (20%): Alignment between section type (methodology, results, etc.) and job requirements
- Expertise Alignment (15%): How well section content matches persona expertise areas
- Structural Importance (5%): Document hierarchy and section positioning weights
From top-ranked sections, we extract granular subsections using paragraph boundaries and sentence clustering. Each subsection is scored for relevance and filtered for optimal length and keyword density. This provides actionable, focused content that directly serves the user's needs.
Unlike static keyword matching, our system adapts its scoring criteria based on persona type. A PhD researcher gets methodology-heavy content prioritized, while a business analyst receives results and metrics-focused sections. The content type detection ensures that summarization jobs receive conclusion sections while analysis jobs get methodology and discussion content.
The solution leverages your proven Round 1A extraction capabilities while adding sophisticated intelligence layers. Multi-language OCR support, robust error handling, and efficient processing ensure reliable performance within the 60-second constraint. The modular architecture enables easy debugging and future enhancements.
This approach delivers truly personalized document intelligence that understands both what the user does and what they need to accomplish.