This automation script makes use of Neural Networks to generate a summary of an article. It makes use of web scraping to scrape the contents of the article and then feed them to a pre-trained model that outputs an Abstract summary.

summarizer.py

from transformers import BartForConditionalGeneration, BartTokenizer
import requests
from bs4 import BeautifulSoup

## Function to summarize article
def summarize_article(article_text, max_length=150):
    model_name = "facebook/bart-large-cnn"
    tokenizer = BartTokenizer.from_pretrained(model_name)
    model = BartForConditionalGeneration.from_pretrained(model_name)

    inputs = tokenizer.encode("summarize: " + article_text, return_tensors="pt", max_length=1024, truncation=True)
    summary_ids = model.generate(inputs, max_length=max_length, min_length=50, length_penalty=2.0, num_beams=4, early_stopping=True)

    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    return summary

## Function to scrape content of the aricle
def scrape_webpage(url):
    try:
        response = requests.get(url)
        response.raise_for_status()
        soup = BeautifulSoup(response.text, 'html.parser')
        all_text = soup.get_text(separator='\n', strip=True)
        return all_text
    except requests.exceptions.RequestException as e:
        print(f"Error: {e}")
        return None

if __name__ == "__main__":
    import numpy as np
    import torch

    print("NumPy version:", np.__version__)
    print("PyTorch version:", torch.__version__)

    webpage_url = "https://www.scmagazine.com/brief/info-crypto-stealing-python-packages-proliferate" ## Sample URL for Testing The Script
    webpage_text = scrape_webpage(webpage_url)

    if webpage_text:
        summary = summarize_article(webpage_text)
        print("\nSummarized Article:")
        print(summary)
    else:
        print("Webpage scraping failed.")