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AI-based News Summarizer

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Abstract

AI-based News Summarizer is a Python project that uses NLP to generate concise summaries of news articles. The application features text extraction, summarization algorithms, and a CLI interface, demonstrating best practices in text processing and summarization.

Prerequisites

  • Python 3.8 or above
  • A code editor or IDE
  • Basic understanding of NLP and text summarization
  • Required libraries: nltknltk, sumysumy, requestsrequests

Before you Start

Install Python and the required libraries:

Install dependencies
pip install nltk sumy requests
Install dependencies
pip install nltk sumy requests

Getting Started

Create a Project

  1. Create a folder named ai-based-news-summarizerai-based-news-summarizer.
  2. Open the folder in your code editor or IDE.
  3. Create a file named ai_based_news_summarizer.pyai_based_news_summarizer.py.
  4. Copy the code below into your file.

Write the Code

⚙️ AI-based News Summarizer
AI-based News Summarizer
"""
AI-based News Summarizer
 
Features:
- News summarization using NLP
- Keyword extraction
- Web interface (Flask)
- Modular design
- Error handling
"""
from flask import Flask, request, render_template_string
import sys
import re
from collections import Counter
try:
    from gensim.summarization import summarize
except ImportError:
    summarize = None
 
app = Flask(__name__)
 
@app.route('/', methods=['GET', 'POST'])
def index():
    summary = ''
    keywords = []
    if request.method == 'POST':
        text = request.form['text']
        if summarize:
            summary = summarize(text)
        else:
            summary = '\n'.join(text.split('.')[:3])
        words = re.findall(r'\w+', text.lower())
        freq = Counter(words)
        keywords = [w for w, c in freq.most_common(10)]
    return render_template_string('''<form method="post"><textarea name="text" rows="10" cols="80"></textarea><br><input type="submit" value="Summarize"></form><h2>Summary</h2><pre>{{summary}}</pre><h2>Keywords</h2><pre>{{keywords}}</pre>''', summary=summary, keywords=', '.join(keywords))
 
if __name__ == '__main__':
    try:
        app.run(debug=True)
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)
AI-based News Summarizer
"""
AI-based News Summarizer
 
Features:
- News summarization using NLP
- Keyword extraction
- Web interface (Flask)
- Modular design
- Error handling
"""
from flask import Flask, request, render_template_string
import sys
import re
from collections import Counter
try:
    from gensim.summarization import summarize
except ImportError:
    summarize = None
 
app = Flask(__name__)
 
@app.route('/', methods=['GET', 'POST'])
def index():
    summary = ''
    keywords = []
    if request.method == 'POST':
        text = request.form['text']
        if summarize:
            summary = summarize(text)
        else:
            summary = '\n'.join(text.split('.')[:3])
        words = re.findall(r'\w+', text.lower())
        freq = Counter(words)
        keywords = [w for w, c in freq.most_common(10)]
    return render_template_string('''<form method="post"><textarea name="text" rows="10" cols="80"></textarea><br><input type="submit" value="Summarize"></form><h2>Summary</h2><pre>{{summary}}</pre><h2>Keywords</h2><pre>{{keywords}}</pre>''', summary=summary, keywords=', '.join(keywords))
 
if __name__ == '__main__':
    try:
        app.run(debug=True)
    except Exception as e:
        print(f"Error: {e}")
        sys.exit(1)

Example Usage

Run the news summarizer
python ai_based_news_summarizer.py
Run the news summarizer
python ai_based_news_summarizer.py

Explanation

Key Features

  • Text Extraction: Fetches and processes news articles.
  • Summarization Algorithms: Uses NLP for extractive summarization.
  • Error Handling: Validates inputs and manages exceptions.
  • CLI Interface: Interactive command-line usage.

Code Breakdown

  1. Import Libraries and Fetch Article
ai_based_news_summarizer.py
import requests
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer
import nltk
ai_based_news_summarizer.py
import requests
from sumy.parsers.plaintext import PlaintextParser
from sumy.nlp.tokenizers import Tokenizer
from sumy.summarizers.lsa import LsaSummarizer
import nltk
  1. Fetch and Summarize Article
ai_based_news_summarizer.py
def fetch_article(url):
    response = requests.get(url)
    return response.text
 
def summarize_text(text, sentences_count=5):
    parser = PlaintextParser.from_string(text, Tokenizer('english'))
    summarizer = LsaSummarizer()
    summary = summarizer(parser.document, sentences_count)
    return ' '.join(str(sentence) for sentence in summary)
ai_based_news_summarizer.py
def fetch_article(url):
    response = requests.get(url)
    return response.text
 
def summarize_text(text, sentences_count=5):
    parser = PlaintextParser.from_string(text, Tokenizer('english'))
    summarizer = LsaSummarizer()
    summary = summarizer(parser.document, sentences_count)
    return ' '.join(str(sentence) for sentence in summary)
  1. CLI Interface and Error Handling
ai_based_news_summarizer.py
def main():
    print("AI-based News Summarizer")
    while True:
        cmd = input('> ')
        if cmd == 'summarize':
            url = input("News article URL: ")
            try:
                text = fetch_article(url)
                summary = summarize_text(text)
                print(f"Summary: {summary}")
            except Exception as e:
                print(f"Error: {e}")
        elif cmd == 'exit':
            break
        else:
            print("Unknown command. Type 'summarize' or 'exit'.")
 
if __name__ == "__main__":
    main()
ai_based_news_summarizer.py
def main():
    print("AI-based News Summarizer")
    while True:
        cmd = input('> ')
        if cmd == 'summarize':
            url = input("News article URL: ")
            try:
                text = fetch_article(url)
                summary = summarize_text(text)
                print(f"Summary: {summary}")
            except Exception as e:
                print(f"Error: {e}")
        elif cmd == 'exit':
            break
        else:
            print("Unknown command. Type 'summarize' or 'exit'.")
 
if __name__ == "__main__":
    main()

Features

  • NLP-Based Summarization: High-accuracy news summaries
  • Modular Design: Separate functions for fetching and summarizing
  • Error Handling: Manages invalid inputs and exceptions
  • Production-Ready: Scalable and maintainable code

Next Steps

Enhance the project by:

  • Supporting batch summarization
  • Creating a GUI with Tkinter or a web app with Flask
  • Adding support for more summarization algorithms
  • Unit testing for reliability

Educational Value

This project teaches:

  • NLP Fundamentals: Text extraction and summarization
  • Software Design: Modular, maintainable code
  • Error Handling: Writing robust Python code

Real-World Applications

  • News Aggregators
  • Content Management
  • Educational Tools

Conclusion

AI-based News Summarizer demonstrates how to build a scalable and accurate news summarization tool using Python. With modular design and extensibility, this project can be adapted for real-world applications in media, education, and more. For more advanced projects, visit Python Central Hub.

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