Text summarization methods have attracted much attention all the time. In recent years, deep learning has been applied to text summarization, and it turned out to be pretty effective. However, most of the current text summarization methods based on deep learning need large-scale datasets, which is difficult to achieve in practical applications. In this paper, an unsupervised extractive text summarization method based on multi-round calculation is proposed. Based on the directed graph algorithm, we change the traditional method of calculating the sentence ranking at one time to multi-round calculation, and the summary sentences are dynamically optimized after each round of calculation to better match the characteristics of the text. In this paper, experiments are carried out on four data sets, each separately containing Chinese, English, long and short texts. The experiment results show that our method has better performance than both baseline methods and other unsupervised methods and is robust on different datasets.
翻译:文本总和方法一直引起人们的极大注意。 近年来,对文本总和应用了深层次的学习,结果证明非常有效。 但是,目前基于深层次学习的文本总和方法大多需要大型数据集,这在实际应用中难以实现。在本文中,根据多轮计算,提出了一种不受监督的抽取文本总和方法。根据定向图表算法,我们将一次计算句次的传统方法改为多轮计算,在每轮计算后,对摘要句子进行动态优化,以更好地匹配文本的特性。在本文中,对四个数据集进行了实验,每个数据集分别包含中文、英文、长篇和短篇文本。实验结果表明,我们的方法比基线方法和其他不受监督的方法都好,并且对不同的数据集非常可靠。