1. Abstract

The Long-Document Information Extraction Using LLMs system is an AI-powered application designed to extract important information from lengthy documents using Large Language Models (LLMs) and Natural Language Processing (NLP). The system processes documents such as research papers, business reports, legal agreements, technical manuals, and financial reports to identify relevant entities, dates, amounts, relationships, key facts, and domain-specific information. It uses document parsing, intelligent text chunking, and structured prompting to handle documents that exceed a model's context window. The extracted information is organized into structured formats such as JSON, CSV, or tables, with source references to help users verify the results. The application aims to reduce manual document analysis time and improve access to information contained in large volumes of text.

2. Objectives

1. To automate information extraction from lengthy documents.
2. To process PDF, DOCX, and TXT files.
3. To identify important entities, dates, amounts, and key facts.
4. To extract domain-specific information using LLMs.
5. To handle long documents through intelligent text chunking.
6. To generate structured outputs in JSON, CSV, and table formats.
7. To link extracted information to relevant source passages.
8. To reduce manual document-reading and data-entry effort.
9. To evaluate extraction accuracy, completeness, and consistency.

3. Existing System

Traditional information extraction systems rely on manual document review, keyword searches, regular expressions, and rule-based NLP methods. These approaches can work well for predictable formats but may struggle with complex sentences, varying document structures, and information expressed in different ways. Generic LLM applications can extract information from text, but processing very long documents may exceed context limits or result in missed details and unsupported outputs.

Disadvantages

1. Manual extraction is time-consuming.
2. Rule-based methods may fail when document formats change.
3. Important information may be spread across multiple sections.
4. Long documents may exceed model context limits.
5. Generic AI responses may contain inaccurate or unsupported details.
6. Extracted information may lack source references.
7. Large-scale document processing can be difficult to manage consistently.

4. Proposed System

The proposed system combines document processing, intelligent chunking, LLM-based extraction, and structured output validation. Users upload a document and specify the information they want to extract, such as names, dates, financial figures, project details, obligations, or research findings.

The application extracts text, divides it into manageable chunks while preserving document structure, and processes each chunk using an LLM. The extracted results are merged, deduplicated, validated against a predefined schema, and displayed in a structured format. Source passages or page references are included where available so users can verify extracted facts against the original document.

Key Features

1. Long-document upload and processing.
2. Support for PDF, DOCX, and TXT files.
3. Configurable information extraction fields.
4. LLM-based entity and fact extraction.
5. Intelligent text chunking and section processing.
6. Structured JSON and CSV output.
7. Duplicate detection and result consolidation.
8. Source passage and page-reference tracking.
9. Schema validation and missing-field detection.
10. Search, filtering, and report export.

5. Implementation Procedure

1. Collect sample documents: Prepare research papers, reports, contracts, manuals, or other documents appropriate to the selected use case.

2. Build the upload interface: Allow users to upload documents and define the information fields they want to extract.

3. Extract document text: Use PyMuPDF or pdfplumber for PDFs and python-docx for Word documents. Apply OCR when processing scanned files.

4. Preprocess the content: Clean extraction artifacts, preserve headings and page numbers, and identify document sections.

5. Implement intelligent chunking: Divide the text into overlapping chunks based on token limits, paragraph boundaries, or document structure.

6. Integrate an LLM: Connect to a compatible hosted model or locally deployed LLM for information extraction.

7. Design extraction prompts: Specify the required fields, output schema, evidence requirements, and instructions for handling missing or ambiguous information.

8. Extract information: Process each chunk and generate structured records containing extracted values and supporting text.

9. Merge and validate results: Combine records from different chunks, remove duplicates, validate data types, and resolve conflicting values where possible.

10. Track source references: Store page numbers, section headings, or source passages alongside extracted information to support verification.

11. Build the results dashboard: Display extracted entities, facts, and relationships in searchable tables with options to review the source text.

12. Export the results: Allow users to download the extracted information in JSON, CSV, or other suitable formats.

13. Evaluate the system: Measure precision, recall, extraction completeness, schema validity, source-reference accuracy, and processing time against manually annotated examples.

14. Deploy securely: Protect uploaded documents, manage access permissions, and configure suitable retention policies for sensitive information.

6. Software Requirements

7. Hardware Requirements

Note: Processing very long documents may require additional memory and time, particularly for scanned PDFs or locally hosted language models.

8. Advantages of the Project

1. Time efficiency: Automates repetitive document-reading and data-extraction tasks.
2. Long-document support: Processes large documents through chunking and section-based analysis.
3. Flexible extraction: Supports user-defined fields across different document types.
4. Structured information: Converts unstructured text into organized records.
5. Source traceability: Helps users verify results against original passages.
6. Reduced manual effort: Minimizes repetitive data entry and searching.
7. Reusable outputs: Supports downstream analytics and database integration.
8. Improved consistency: Applies a repeatable extraction workflow.
9. Extensible design: New extraction fields and document categories can be added.
10. Quality validation: Helps detect malformed records, missing values, and duplicate results.