Document Summerization with GenAI

1. Problem Statement

Organizations deal with large volumes of unstructured documents (policies, contracts, research papers, regulatory docs, etc.). Manually summarizing them is time-consuming, inconsistent, and costly.A GenAI-powered summarization solution can extract key insights, generate concise summaries, and even adapt the tone (executive summary vs. detailed analysis).

2. High-Level GenAI Summarization Solution

🔹 Steps

1. Document Ingestion

   • Upload docs (PDF, Word, PPT, Emails, Scanned Images).

   • Extract text using OCR (if scanned).

   • Store metadata for indexing.

2. Pre-Processing

   • Clean text (remove boilerplate, stop words, duplicates).

   • Chunk documents into smaller segments (e.g., 1,000 tokens each).

   • Store in Vector DB with embeddings.

3. Embedding & Indexing

   • Use embedding models (e.g., OpenAI text-embedding-3-large, HuggingFace models).

   • Index embeddings in Vector DB (Pinecone, Weaviate, Milvus, FAISS, Azure Cognitive Search).

4. Summarization Process

   • Direct Summarization: For short docs, directly send to LLM for summarization.

   • RAG-based Summarization: For large docs:

     • Retrieve relevant chunks from Vector DB.

     • Provide context + summarization prompt to LLM.

   • Summarization Styles:

     • Abstractive (LLM generates new concise sentences).

     • Extractive (pulls key sentences verbatim).

     • Hybrid (both).

5. Summary Generation

   • Types of summaries:

     • Extractive Summary (key sentences).

     • Abstractive Summary (human-like, paraphrased).

     • Hierarchical Summary (Executive summary → Section summary → Paragraph summary).

     • Query-based Summary (e.g., "Summarize this contract focusing on risks and obligations").

   • 
     

6. Output & Delivery

   • Provide multiple views:

     • Executive summary (1–2 paragraphs).

     • Detailed summary (bullets per section).

     • Action items / Key insights.

   • Deliver via:

     • Web app / dashboard

     • API integration (CRM, DMS, Banking platforms)

     • Email / PDF export

3. Architecture Flow (Text Version)

[Document Sources: PDFs, Emails, Policies, Contracts]
        |
        v
 [OCR / Text Extraction]
        |
        v
 [Pre-Processing & Chunking]
        |
        v
 [Embedding Model] ---> [Vector DB / Index]
        |
        v
 [Retrieval (RAG)] + [Summarization Prompt to LLM]
        |
        v
 [GenAI LLM: GPT-4, Claude, LLaMA2, Azure OpenAI]
        |
        v
 [Summaries: Executive | Detailed | Action Items]
        |
        v
 [Delivery: Web App, API, Reports]

4. Sample Tech Stack

• Ingestion: Apache Tika, Azure Form Recognizer, AWS Textract, GCP Document AI

• Pre-processing: Python (NLTK, spaCy), LangChain, LlamaIndex

• Vector DB: Pinecone / Weaviate / Milvus / FAISS / Azure Cognitive Search

• LLM / GenAI: OpenAI GPT-4, Anthropic Claude, LLaMA2, Mistral

• Orchestration: LangChain, Haystack, Semantic Kernel

• Delivery: Web (React/Angular), API Gateway, Chatbot integration

5. Use Cases

• Banking / BFSI → Summarize regulatory compliance docs, credit risk assessments, loan applications.

• Legal → Summarize contracts with key clauses & risks.

• Healthcare → Summarize patient history & clinical notes.

• Research → Summarize academic papers into insights.

• CXO Dashboards → Daily executive briefings from long reports.

✅ This makes it scalable, secure, and customizable for different domains.If you like, I can also create a sample prompt framework for summarization (executive summary, detailed breakdown, key risks) — so you can reuse in interviews or POCs.

1. Problem Statement

Organizations deal with huge amounts of unstructured documents (policies, contracts, research papers, compliance docs, meeting transcripts, etc.). Manual reading is time-consuming and error-prone.Goal → Automatically generate concise, context-aware summaries (extractive or abstractive) using Generative AI.

2. Traditional vs GenAI Summarization

• Traditional Extractive Methods:

  • TF-IDF, TextRank → pick important sentences but lack context, coherence, or rephrasing.

• GenAI Methods:

  • Use LLMs (like GPT, LLaMA, Claude, etc.) to understand, paraphrase, and abstract key ideas.

  • Generates abstractive summaries that read naturally, not just copy-paste.

3. GenAI Solution Architecture

Here’s a typical enterprise-ready summarization pipeline:

1. Document Ingestion Layer

   • Sources: PDFs, Word, PPT, Emails, Knowledge Base, Scanned Docs (OCR if needed).

   • Store in Blob storage / Document DB (e.g., Azure Blob, AWS S3, MongoDB).

2. Preprocessing

   • Convert docs to text (PDF parser, OCR).

   • Clean & normalize (remove headers, footers, duplicates).

   • Split into chunks (e.g., 1,000 tokens) for LLM efficiency.

3. Embedding & Indexing (Optional if RAG-based)

   • Use vector DB (Pinecone, Weaviate, Milvus, FAISS, Azure AI Search).

   • Store embeddings for semantic retrieval.

4. Summarization ProcessTwo modes:

   • Direct Prompting (Small docs) → Send doc text to LLM with summarization prompt.

   • RAG + GenAI (Large docs) →

     • Retrieve most relevant chunks from vector DB.

     • Send them to LLM with summarization instructions.

5. Summary Generation

   • Types of summaries:

     • Extractive Summary (key sentences).

     • Abstractive Summary (human-like, paraphrased).

     • Hierarchical Summary (Executive summary → Section summary → Paragraph summary).

     • Query-based Summary (e.g., "Summarize this contract focusing on risks and obligations").

6. Post-processing

   • Check for hallucinations using fact verification (compare against original text).

   • Add metadata (title, doc ID, author, timestamp).

   • Store summaries in knowledge base / DB.

7. Delivery

   • Present via Dashboard, API, Chatbot, or Integration (Teams, Slack, Outlook, Banking App, etc.).

4. GenAI Tech Stack

• LLM Models: GPT-4, Claude, LLaMA2, Mistral, Falcon, or domain-tuned LLM.

• Vector DB: Pinecone, Weaviate, FAISS, Azure Cognitive Search, AWS Kendra.

• Orchestration: LangChain, LlamaIndex, Semantic Kernel.

• Deployment: Azure AI Services, AWS Bedrock, GCP Vertex AI, On-prem (private LLM).

• Governance: Data masking, PII removal, Zero-trust policies.

5. Example Use Cases

• Banking: Summarize KYC docs, loan agreements, compliance updates.

• Legal: Extract obligations, risks, and key clauses from contracts.

• Healthcare: Summarize patient records, discharge notes.

• Corporate: Auto-generate MoM (Minutes of Meetings).

• Research: Summarize 100s of academic papers into a literature review.

6. Sample Prompt for Summarization

You are an expert summarizer. Summarize the following document into 3 sections:
1. Executive Summary (2-3 lines)
2. Key Points (bullet points)
3. Risks or Action Items

Document: [insert document text/chunks here]

✅ End Result: Instead of spending hours, users get an accurate, human-like, context-aware summary in seconds.