title	emoji	colorFrom	colorTo	sdk	app_port	pinned
Multi-Document Summarizer	📚	blue	green	docker	8000	true

Hybrid Multi-Document Summarization System

A full-stack application that effectively summarizes information from multiple documents, focusing on key topics and reducing redundancy. This project uses a hybrid extractive-abstractive approach to generate coherent and concise summaries.

https://huggingface.co/spaces/NeelM47/summarizer

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Features

• Multi-Document Input: A dynamic web interface that allows users to input multiple documents for summarization.
• Configurable Topic Granularity: Users can select the number of topics (clusters) to extract, controlling the detail level of the summary.
• Redundancy Reduction: Uses sentence-transformers to create semantic embeddings and scikit-learn KMeans clustering to identify unique topics across all documents.
• Coherent Summaries: Employs a pre-trained BART model from Hugging Face transformers to generate a fluent, abstractive summary from the most representative sentences.
• RESTful API: The core NLP logic is exposed via a robust and fast FastAPI backend.
• Containerized Application: The entire application is containerized with Docker, ensuring easy and reproducible deployment.

Technology Stack

• Backend: Python 3.9, FastAPI
• NLP/ML: Hugging Face transformers, sentence-transformers, scikit-learn, nltk
• Frontend: HTML, CSS, Vanilla JavaScript
• Deployment: Docker, Uvicorn, Hugging Face Spaces

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How It Works: The Hybrid Approach

1. Ingestion: The system takes a list of documents and a desired number of topics (k) as input.
2. Sentence Cleaning & Embedding: All documents are split into sentences. Each sentence is cleaned (lowercased, punctuation removed) and then converted into a high-dimensional vector embedding using a Sentence-Transformer model.
3. Extractive Clustering: The sentence embeddings are clustered using KMeans with k clusters. This groups semantically similar sentences into topics.
4. Centroid-Based Selection: The most representative sentence from each cluster is selected by finding the one closest to the cluster's centroid.
5. Abstractive Summarization: These selected, non-redundant sentences are combined and passed to a BART model, which generates a final, coherent summary.

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Running the Project Locally

1. Using Conda (for development)

# Clone the repository
git clone https://github.com/NeelM47/summarizer.git
cd summarizer

# Create and activate the conda environment
conda env create -f environment.yml 
conda activate summarizer

# Run the FastAPI server
uvicorn main:app --reload

Access the app at http://127.0.0.1:8000

2. Using Docker (for production/deployment)

Ensure you have Docker installed and running.

# Clone the repository
git clone https://github.com/NeelM47/summarizer.git
<<<<<<< HEAD
cd summarizer
=======
cd suvidha-summarizer
>>>>>>> 1d551d7cc667304ddd3e9d9a672bebd1f68bc829

# Build the Docker image
docker build -t summarizer .

# Run the Docker container
docker run -p 8000:8000 summarizer

Access the app at http://127.0.0.1:8000

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API Documentation

Once the server is running, interactive API documentation (powered by Swagger UI) is available at http://127.0.0.1:8000/docs.