code: def vote(self):
          
  
          # 初始化
          weight_queue = []
          sameKV = 0
          count = 0
          # 相似度计算
          for unit in self._queue:
              unit_set = convert_to_set(unit)
              for i in unit_set:
                  if i in self.wordvector_word:
                      sameKV += self.wordvector_weight[self.wordvector_word.index(i)]
  
              if len(self._queue) >= 5:
                  # k是位置权重，离语料库越近的文本行，权重越大，区间【0，1】
                  k = (count + 1) / len(self._queue)
                  beta = normalized(self.beta_list[count], self.beta_list)
                  count += 1
              else:
                  k = 1
                  beta = normalized(self.beta_list[count], self.beta_list)
                  count += 1
              jaccard = sameKV / len(
                  (set(unit_set) | set(self.wordvector_word)) - (set(unit_set) & set(self.wordvector_word)))
              unit_weight = k * beta * jaccard
              weight_queue.append(unit_weight)
              sameKV = 0
              log('debug',
                  '文本行【{}】\n相似度计算参数，unit_weight:【{}】，k:【{}】,beta:【{}】,jaccard:【{}】\n'.format(unit, unit_weight, k, beta,
                                                                                             jaccard))
  
          # 过滤
          try:
              title = self._queue[weight_queue.index(sorted(weight_queue, reverse=True)[0])]
          except:
              title = ''
  
          return title
docs: 选举新闻标题
  
          Return:
          title               -- 新闻标题，str类型