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Smart Summarizer
Introduction
Smart Summarizer is an AI-powered content summarization tool that condenses lengthy documents, articles, and reports into concise, structured bullet points. Built with Streamlit and powered by Google’s Long-T5 model, it provides multi-input summarization from raw text, PDF files, and web articles—making it ideal for researchers, students, professionals, and content consumers.
Problem Statement
With the overwhelming volume of digital content—academic papers, long-form articles, and technical documents—users often struggle to extract key insights efficiently. Traditional summarization tools either lack flexibility or fail to retain contextual accuracy, especially when handling long documents or multi-source inputs.
Objectives
- Enable users to summarize long-form content from PDFs, URLs, or pasted text.
- Allow customization of summary length based on desired number of points.
- Support contextual guidance using user-provided notes to tailor the summary.
- Provide a fast, intuitive UI for real-time summarization using modern NLP models.
Product/Project Analysis
The tool supports three primary input modes:
- Text Input: Direct user-pasted content.
- PDF Upload: Academic papers, reports, etc.
- Web Link: Articles/blogs fetched from online sources.
It integrates Hugging Face’s transformers library for inference using google/long-t5-tglobal-base, which supports long sequence inputs and outputs structured summaries. Users can control output verbosity, add contextual notes, and receive summaries in seconds.
Architecture
📥 User Input
Pasted Text, Uploaded PDF, or Web URL
📄 Text Extraction
Text via fitz (PDF) or newspaper3k (URL)
📝 Notes (Optional)
User-provided context merged with extracted text
🔠 Tokenization
Handled by Hugging Face's AutoTokenizer
🧠 Long-T5 Model
google/long-t5-tglobal-base for long-sequence summarization
📋 Summary Output
Bullet-point summary based on user-defined length
A streamlined NLP pipeline that transforms long-form content into digestible summaries.
Impact
- Saves time by reducing hours of reading to a few bullet points.
- Enhances productivity for students, researchers, analysts, and content creators.
- Customization options provide more relevant summaries tailored to the user's intent.
- Multi-source input support makes it flexible and widely applicable across domains.
Conclusion
Smart Summarizer blends state-of-the-art NLP with user-friendly design to solve a real-world need: extracting meaningful insights from overwhelming information. Its modular architecture, input flexibility, and customization features make it a valuable productivity tool for anyone working with long-form content.