Our task is to generate an effective summary for a given document with specific realtime requirements. We use the softplus function to enhance keyword rankings to favor important sentences, based on which we present a number of summarization algorithms using various keyword extraction and topic clustering methods. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. We show that our algorithms meet the realtime requirements and yield the best ROUGE recall scores on DUC-02 over all previously-known algorithms. To evaluate the quality of summaries without human-generated benchmarks, we define a measure called WESM based on word-embedding using Word Mover's Distance. We show that the orderings of the ROUGE and WESM scores of our algorithms are highly comparable, suggesting that WESM may serve as a viable alternative for measuring the quality of a summary.

翻译：我们的任务是为具有具体实时要求的某一文件生成有效的摘要。 我们使用软附加功能来提高关键词排序,以有利于重要的句子, 我们根据这些功能, 使用各种关键词提取和主题组合方法, 提出一些汇总算法。 我们显示我们的算法符合实时要求, 并在 DUC-02 对所有以前已知的算法中产生最佳的ROUGE回溯分数。 我们显示我们的算法符合实时要求, 在所有以前已知的算法中产生最佳的ROUGE/ 02回溯ROUGE的分数。 为了评估没有人为基准的摘要质量, 我们根据WOW Moler的距离, 定义了一个称为WESM的措施。 我们显示,我们的算法中ROUGE和WESM的分数非常相似, 表明WESM可以作为衡量摘要质量的可行替代方法。