Text Summarization API

This project is a FastAPI-based text summarization service that supports multiple traditional summarization algorithms from the sumy library. It is not based on large language models (LLMs) but uses classical approaches for summarization.

Features

• Supports various summarization algorithms:
  • SumBasic
  • Luhn
  • Edmundson
  • LexRank
  • TextRank
  • LSA (Latent Semantic Analysis)
• Cleans the input text by removing unwanted characters and links.

Requirements

• Python 3.8+
• FastAPI
• Pydantic
• sumy
• nltk
• numpy

Installation

1. Clone the repository:

   git clone github.com/AmirTahaMim/SumSimple
   cd <repository-directory>

2. Create and activate a virtual environment:

   python -m venv env
   source env/bin/activate  # On Windows use `env\Scripts\activate`

3. Install the required packages:

   pip install fastapi pydantic sumy nltk numpy

4. Download the necessary NLTK data:

   python -c "import nltk; nltk.download('punkt')"

Running the Application

1. Run the FastAPI server:

   uvicorn main:app --reload

2. The API will be available at http://127.0.0.1:8000.

API Usage

Endpoint

• POST /summarize/

Request Body

{
  "input_text": "Your base64 encoded text here",
  "summarizer": "LSA",
  "sentences_count": 5,
  "language": "english"
}

• input_text: The text to summarize (base64 encoded).
• summarizer: The summarization algorithm to use (default: LSA). Options: "SumBasic", "Luhn", "Edmundson", "LexRank", "TextRank", "LSA".
• sentences_count: The number of sentences for the summary (default: 5).
• language: The language of the text (default: "english").

Example Request

curl -X POST "http://127.0.0.1:8000/summarize/" -H "Content-Type: application/json" -d '{
  "input_text": "VGhpcyBpcyBhIHNhbXBsZSB0ZXh0IHRvIHN1bW1hcml6ZS4uLi4=",  # Base64 encoded text
  "summarizer": "LSA",
  "sentences_count": 3,
  "language": "english"
}'

Example Response

{
  "summary": "This is the summarized text."
}

Text Cleaning

The input text is cleaned by:

• Removing lines with '*' or fewer than 50 words.
• Removing markdown and HTML links.
• Removing URLs.
• Removing extra whitespace and unwanted characters.

Contributing

Contributions are welcome! Please submit a pull request or open an issue to discuss any changes.

License

This project is licensed under the MIT License.