论文标题
上下文网络和无监督的句子排名
Contextual Networks and Unsupervised Ranking of Sentences
论文作者
论文摘要
我们构建了一个上下文网络，以表示单词句子对之间具有句法和语义关系的文档，基于该文档，我们设计了一种无监督的算法，称为CNATAR（上下文网络和文本分析等级），以分数句子，并通过Bi-Objective 0-1的0-1 knapsack knapsack knapsack最大化问题的问题超过主题分析和句子分析和句子得分。我们表明，CNATAR的表现优于Rouge和BLEU指标下的Summbank数据集中提供的三位人类法官的联合排名，这在术语中大大优于每个法官的排名。此外，CNATAR的生产迄今为止的Rouge得分比DUC-02的得分最高，并且在CNN/Dailymail和NYT数据集上胜过先前的监督算法。我们还比较了CNATAR的性能以及最新的监督神经网络摘要模型和计算甲骨文结果。
We construct a contextual network to represent a document with syntactic and semantic relations between word-sentence pairs, based on which we devise an unsupervised algorithm called CNATAR (Contextual Network And Text Analysis Rank) to score sentences, and rank them through a bi-objective 0-1 knapsack maximization problem over topic analysis and sentence scores. We show that CNATAR outperforms the combined ranking of the three human judges provided on the SummBank dataset under both ROUGE and BLEU metrics, which in term significantly outperforms each individual judge's ranking. Moreover, CNATAR produces so far the highest ROUGE scores over DUC-02, and outperforms previous supervised algorithms on the CNN/DailyMail and NYT datasets. We also compare the performance of CNATAR and the latest supervised neural-network summarization models and compute oracle results.
