text: from nltk import sent_tokenize
  from sklearn.feature_extraction.text import TfidfVectorizer
  from sklearn.metrics.pairwise import cosine_similarity
  from preprocess import pre_process
  
  
  def summary_sents(sub_topic, sents):
      sents = sent_tokenize(' '.join(sents))
      sents.append(sub_topic)
      
      sent_vectorizer = TfidfVectorizer(decode_error='replace',min_df=1, stop_words='english',
                                   use_idf=True, tokenizer=pre_process, ngram_range=(1,3))
  
      sent_tfidf_matrix = sent_vectorizer.fit_transform(sents)
      #subtopic_tfidf = sent_tfidf_matrix.transform([sub_topic])
      sub_topic_similarity = cosine_similarity(sent_tfidf_matrix)
      top10_sents = sub_topic_similarity[-1][:-1].argsort()[:-11:-1]
      final_sents=[]
      for i in top10_sents:
          final_sents.append(sents[i])
      return final_sents
  