Simple Text Summarization with BART

This repository contains a simple text summarization system built with the BART model. The model is pre-trained on the CNN/Daily Mail dataset and can generate a summary for a given piece of text.

Features

• Uses the facebook/bart-large-cnn model from Hugging Face’s transformers library.
• The system takes a piece of text and returns a summarized version of it.
• The summary is generated directly from the input text.

Usage

The main function in this repository is summarize(text). Here’s how to use it:

Python

from transformers import BartTokenizer, BartForConditionalGeneration

Load the pre-trained BART model and tokenizer

model_name = 'facebook/bart-large-cnn'
model = BartForConditionalGeneration.from_pretrained(model_name)
tokenizer = BartTokenizer.from_pretrained(model_name)

Text to summarize

text = """
... Your text here ...
"""

Replace "... Your text here ..." with the text you want to summarize.

main function

def summarize(text):
    # Tokenize the input text
    inputs = tokenizer([text], max_length=1024, return_tensors='pt')

    # Generate a summary
    summary_ids = model.generate(inputs['input_ids'], num_beams=4, length_penalty=2.0, max_length=100, min_length=30, early_stopping=True)

    # Decode the summary
    summary = [tokenizer.decode(g, skip_special_tokens=True, clean_up_tokenization_spaces=False) for g in summary_ids]

    return summary

summary = summarize(text)
print("Summary:", summary)

The summarize() function will return a summarized version of the input text.

Requirements

• Python 3.6 or later.
• PyTorch 1.0.0 or later.
• Transformers library from Hugging Face.

Installation

You can install the required packages with pip:

pip install torch transformers