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Joint Lifelong Topic Model and Manifold Ranking for Document Summarization by Lin, Jianying; Liu, Rui; Jia, Quanye is a scholarly article available to read on EtoBox.

What is Joint Lifelong Topic Model and Manifold Ranking for Document Summarization about?

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 be

Author

Lin, Jianying; Liu, Rui; Jia, Quanye

Published

2019

Language

EN