如何使用 lambda_ 参数的值 0.2

如何解决如何使用 lambda_ 参数的值 0.2

我被难住了。我对 Python 和文本挖掘还是很陌生。我知道我需要做的事情非常简单，但我不知道如何使用 lambda_ 参数的值 0.2 生成一篇关于印第安纳州的文章的提取摘要。

我为初学者尝试过这个

#define a function that will generate an extractive summary of the article's text by identifying the best 
#representative,non-redundant sentence in each paragraph; i.e.,the sentence with the Maximum Marginal Relevance (MMR).

def get_extractive_summary(lambda_):

 #NOTE: the lambda parameter controls the level of relevance vs. redundancy among the sentences in the summary
  summary_sentences = []
  for paragraph_id in range(len(paragraphs)): #for each paragraph ID
    
    #extract the TF-IDF scores for this paragraph's sentences from the dataframe
    df_sentences = df[['tfidf_scores']][df.paragraph_id == paragraph_id]
   
    #identify the sentence with the Maximum Marginal Relevance (MMR) for this paragraph
    maximum_marginal_relevance = -np.inf #holds the MMR for the sentences in this paragraph
    best_sentence = None #holds the sentence with the MMR
    for sentence in df_sentences.itertuples(): #for each sentence in this paragraph
     
      #calculate the cosine similarity between this sentence's TF-IDF scores and the article's centroid
      similarity_to_article = cosine_similarity(np.reshape(sentence.tfidf_scores,(1,-1)),centroid)[0][0]
     
      #calculate the maximum cosine similarity between this sentence and any already-chosen summary sentences
      max_similarity_to_summary_sentence = -np.inf
      for sentence_id,summary_sentence_tfidf_scores in summary_sentences:
        similarity_to_summary_sentence = cosine_similarity(np.reshape(sentence.tfidf_scores,np.reshape(summary_sentence_tfidf_scores,-1)))[0][0]
        if similarity_to_summary_sentence > max_similarity_to_summary_sentence:
          max_similarity_to_summary_sentence = similarity_to_summary_sentence
      
      #compute the marginal relevance for this sentence
      marginal_relevance = ((1 - lambda_) * similarity_to_article) - (lambda_ * max_similarity_to_summary_sentence)
      if marginal_relevance > maximum_marginal_relevance:
        maximum_marginal_relevance = marginal_relevance
        best_sentence = (sentence.Index,sentence.tfidf_scores)
    
    
    #add the sentence with the Maximum Marginal Relevance (MMR) for this paragraph to the collection of summary sentences
    summary_sentences.append(best_sentence)

  #construct and return the summary of the article
  article_summary = ''
  for sentence_id,_ in summary_sentences:
    article_summary += df.iloc[sentence_id]['raw_text'] + ' '
  return article_summary.strip()

get_extractive_summary(lambda 0.2)

但它出错并说 lambda 没有定义，但它是由 def 函数定义的。

感谢任何帮助。