give code for textrank algorithm
give code for textrank algorithm in python

TextRank is a popular unsupervised algorithm for extracting key phrases from a given text. Here's a simple implementation of TextRank in Python using NetworkX and NLTK.

```python
import networkx as nx
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer
import re

def preprocess(text):
    stop_words = set(stopwords.words('english'))
    lemmatizer = WordNetLemmatizer()
    words = re.findall(r'\w+', text.lower())
    words = [lemmatizer.lemmatize(word) for word in words if word not in stop_words]
    return words

def text_rank(text, max_words=5):
    G = nx.Graph()
    words = preprocess(text)

    for word in words:
        G.add_node(word)

    for i, word1 in enumerate(words):
        for word2 in words[i+1:]:
            if word1 and word2:
                G.add_edge(word1, word2, weight=text[text.lower().index(word1.lower()) + len(word1.lower())
def textrank(text, terms):
    # Initialize a vector of scores 
    scores = [0] * len(terms)
    # Initialize a vector of indices 
    indices = [0] * len(terms)
    # Initialize a vector of frequencies 
    frequencies = [0] * len(terms)
    # Initialize a vector of term indices 
    term_indices = [0] * len(terms)
    # Initialize a vector of document lengths 
    document_lengths = [len(text)] * len(terms)
    # Initialize a vector of document ranks 
    document_ranks = [0] * len(terms)
    # Initialize a vector of term ranks 
    term_ranks = [0] * len(terms)
    # Initialize a vector of document scores 
    document_scores = [0] * len(terms)
    # Initialize a vector of term scores 
    term_scores = [0] * len(terms)

    # loop over terms 
    for i in range(len(terms)):
        # loop over documents 
        for j in range(len(textrank.documents[i])):
            # loop over words 
            for k in range(len(textrank.documents[i