An implementation of TextRank with cosine similarity. This code is based on graphranker.py (https://gist.github.com/nkt1546789/f5a8f3c5bb4445d141fe7dd03a84bcd1).

textrank.py

import numpy as np
from scipy import sparse
from sklearn import preprocessing
from graphranker import GraphRanker

class TextRank(GraphRanker):
    def fit(self, texts):
        self.texts = texts
        dictionary = {}
        data = []
        row = []
        col = []
        for i, text in enumerate(texts):
            for token in text:
                j = dictionary.setdefault(token, len(dictionary))
                data.append(1.0)
                row.append(i)
                col.append(j)
        X = sparse.csr_matrix(sparse.coo_matrix((data,(row,col))))
        S = X.dot(X.T)
        return super(TextRank, self).fit(S)

    def print_texts(self, topn=10):
        for i in np.argsort(self.f)[::-1][:topn]:
            print self.f[i], u"".join(self.texts[i])