"""
Advanced Text Summarization Module
Generated by Eden via recursive self-improvement
2025-11-01 05:22:21.532553
"""

import nltk
from nltk.tokenize import sent_tokenize, word_tokenize
from nltk.corpus import stopwords
from heapq import nlargest

def advanced_text_summarization(text, max_length=10):
    """
    Generates a summary of the input text using natural language processing techniques.
    
    Parameters:
        text (str): The input text to be summarized.
        max_length (int): The desired number of sentences in the summary. Default is 10.
        
    Returns:
        str: A concise summary of the input text.
    """
    # Tokenize the text into sentences
    sentences = sent_tokenize(text)
    
    # Remove stopwords and tokenize words
    stop_words = set(stopwords.words('english'))
    word_sent = [word_tokenize(sent) for sent in sentences]
    words = [word for sent in word_sent for word in sent if word not in stop_words]
    
    # Calculate the frequency of each word
    freq_dist = nltk.FreqDist(words)
    ranking = {i: freq_dist[word] for i, word in enumerate(words)}
    
    # Get top-ranked sentences based on word frequency
    summary_sentences = nlargest(max_length, ranking, key=ranking.get)
    
    # Join the selected sentences to form a summary
    summary = ' '.join([sentences[idx] for idx in summary_sentences])
    
    return summary

# Example usage:
input_text = "Lorem ipsum dolor sit amet, consectetur adipiscing elit. Nulla facilisi. Donec vitae risus vel ex pellentesque suscipit. Integer nec justo at massa varius interdum. Sed ac semper turpis. In hac habitasse platea dictumst. Nam et tortor non dui tincidunt dignissim sit amet euismod velit. Aliquam auctor libero ut est facilisis, vel pretium nibh elementum. Nullam nec massa at ex suscipit placerat. Sed commodo, turpis in bibendum blandit, sem nunc fermentum enim, et venenatis sapien purus vitae felis."

summary = advanced_text_summarization(input_text)
print("Summary:", summary)