Due to the manifold ranking method has a significant effect on the ranking of unknown data based on known data by using a weighted network, many researchers use the manifold ranking method to solve the document summarization task. However, their models only consider the original features but ignore the semantic features of sentences when they construct the weighted networks for the manifold ranking method. To solve this problem, we proposed two improved models based on the manifold ranking method. One is combining the topic model and manifold ranking method (JTMMR) to solve the document summarization task. This model not only uses the original feature, but also uses the semantic feature to represent the document, which can improve the accuracy of the manifold ranking method. The other one is combining the lifelong topic model and manifold ranking method (JLTMMR). On the basis of the JTMMR, this model adds the constraint of knowledge to improve the quality of the topic. At the same time, we also add the constraint of the relationship between documents to dig out a better document semantic features. The JTMMR model can improve the effect of the manifold ranking method by using the better semantic feature. Experiments show that our models can achieve a better result than other baseline models for multi-document summarization task. At the same time, our models also have a good performance on the single document summarization task. After combining with a few basic surface features, our model significantly outperforms some model based on deep learning in recent years. After that, we also do an exploring work for lifelong machine learning by analyzing the effect of adding feedback. Experiments show that the effect of adding feedback to our model is significant.
翻译:由于采用多重排序方法,许多研究人员使用加权网络,使用多重排序方法解决文档总和任务,从而对基于已知数据的未知数据的排序产生重大影响。然而,他们的模型只考虑原始特征,而当他们为多重排序方法建立加权网络时,则忽略了句子的语义特征。为了解决这个问题,我们建议了基于多重排序方法的两个改进模型。一个将主题模型和多重排序方法(JTMMR)结合起来,以解决文件总和任务。这个模型不仅使用原始特征,而且还使用语义特征来代表文档,这可以提高多重排序方法的准确性。另一个模型将终生主题模型和多重排序方法(JLTMMR)结合起来。根据JTMMR,这个模型增加了知识的制约,以提高专题质量。同时,我们还增加了两个文件模型之间的关系的制约,以在文件总和后挖掘更好的文件总和特征。JTMMR模型可以使用更好的语义特征来改进多重排序方法的效应,从而改进了文档的精确性能。在实验后,我们将一个基于一个高级模型的模型和一个高级的模型显示一个更好的模拟,一个高级的模拟,在时间模型上,一个比一个高级的模型可以显示一个比一个高级的模拟的模拟的模拟的模拟的模拟的最近的工作。