选举新闻标题

        Return:
        title               -- 新闻标题，str类型

def vote(self):
        """选举新闻标题

        Return:
        title               -- 新闻标题，str类型
        """

        # 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