Bangla Summarizer

Empowering Bangla readers and writers

A powerful open-source tool designed to generate concise summaries from Bangla (Bengali) text. It leverages advanced NLP techniques to make lengthy content accessible.

Overview

Bangla Summarizer is a Python-based library and CLI tool that solves the challenge of reading lengthy Bangla documents. Whether you are an educator, student, or researcher, this tool helps you digest content quickly by extracting the most meaningful sentences.


The project supports easy integration as a Python library for developers and offers a straightforward command-line interface for general users. It is open-source and free for both commercial and personal use.

Features

What makes this tool special.

Automatic Summarization

Create short, meaningful summaries from long Bangla articles and input files instantly.

Python API & CLI

Flexible usage: import it as a Python module in your scripts or run it directly from the terminal.

Customizable Output

Adjust the summary length dynamically to fit your specific requirements.

Open Source

Licensed under MIT. Free to use, modify, and distribute for personal or commercial projects.

Installation & Usage

Requires Python 3.10+

1. Setup & Install

bash
# Clone the repository
git clone https://github.com/kawshik-ornob8/bangla_summarizer.git
cd bangla_summarizer

# Create virtual environment (Recommended)
python3.10 -m venv venv
source venv/bin/activate # Windows: venv\Scripts\activate

# Install dependencies
pip install -r requirements.txt

2. Run via CLI

bash
# Basic CLI format
python main.py --input path/to/file.txt --output result.txt --length 100

# Example execution
python main.py --input sample_bangla.txt --output summary.txt --length 100

3. Use in Python Script

python
from bangla_summarizer import summarize

bangla_text = "বাংলাদেশ দক্ষিণ এশিয়ার একটি দেশ। এটি একটি সুন্দর প্রকৃতির দেশ..."

# Generate summary with max length of 100
summary = summarize(bangla_text, max_length=100)

print(summary)

Requirements & Structure

The project requires Python 3.10 strictly. It is organized into a modular structure for easy maintenance and testing.

Python 3.10
CLI
Virtual Env
Git
NLP