This repository contains the code of a user interface using Django where pdf document can be summarized.
In this project we have embarked to harness the power of Artificial Intelligence (AI) to revolutionize he way we interact with textual information. Our project, titled “AI-Powered Summarization” seeks to employ cutting-edge AI technologies to automatically generate concise and coherent summaries from PDF documents.
In this project we have done an AI powered solution for document summarization. Which can simplify document by automatically drafting legal documents in plain language and using easy-to-understand terms.
Target audience: Individuals and small businesses with limited access to legal resources. Main objective: Make legal documents more accessible and understandable.
Natural Language Processing (NLP) Frameworks: HuggingFace Transformer : Inorder to process the text from the document and summarize, make it more understandable.
PyPDF2: For extracting text and data from PDF documents. Python-docx: For working with Microsoft Word documents.
Frontend Framework: HTML, CSS, JavaScript for the user interface. Backend Framework: Django (Python) for server-side logic. Database: MySQL for storing user data and documents. Hosting and Development: Consider cloud platforms for hosting and development.
Git: Used for version control and tracking changes to the codebase.
In this project we have done an AI powered solution for document summarization. Which can simplify document by automatically drafting legal documents in plain language and using easy-to-understand terms.
Develop a user-friendly web application using technologies like HTML, CSS and JavaScript. Connect all the frontend using Django framework. When a user uploads a PDF document, The system should parse the PDF, preprocess the text and pass it through The trained summarization model. Generate a summary of the document and present it to the user. Implement PDF parsing using the PyPDF library to extract text and structural information from PDF files. Add tokenization and learning are done by Hugging face transformer.
By providing users with summarized versions of PDF documents, the project can significantly increase the efficiency of information retrieval and comprehension. Users can quickly assess the relevance and key points of a document, saving time and effort. Summarized content can make information more accessible to a broader audience, including individuals with limited time or those who may struggle with reading extensive documents, such as students, researchers, and professionals. The project can help identify and prioritize essential information within documents, facilitating better decision-making and prioritization of tasks.
The project on AI-powered summarization of PDFs represents a significant step towards making information more accessible, efficient and user-friendly in digital age. The summarization process streamlines the extraction of key insight, making it easier for users to comprehend, prioritize and share information. Continuously improving the AI summarization model by fine-tuning it with more extensive datasets and more advanced transformer architectures. This can lead to even more accurate and contextually aware summaries. Expanding the project's capabilities to support multiple languages, allowing users to summarize documents in various languages.
Expanding the project to support multiple language, allowing the user to summarize documents in various language.