This paper propose a method for sentence ordering in multi-document summarization task, which combine support vector machine (SVM) and Grey Model(GM). Firstly, the method train the SVM with sentences ...of source documents and predict sentences sequence of summary as primary dataset. Secondly, using Grey Model to process the primary dataset, and achieve the final sequence of summary sentences. Experiments on 100 summaries showed this method provide a much higher precision than probabilistic model in sentence ordering task.
In this paper, we present a practical method of sentence ordering in multi-document summarization tasks of Chinese language. By using Support Vector Machine (SVM), we classify the sentences of a ...summary into several groups in rough position according to the source documents. Then we adjust the sentence sequence of each group according to the estimation of directional relativity of adjacent sentences, and find the sequence of each group. Finally, we connect the sequences of different groups to generate the final order of the summary. Experimental results indicate that this method works better than most existing methods of sentence ordering.
Temporal XML Index Schema Ye, Xiaoping; Luo, Junjie; Zhong, Gongfu
Temporal Information Processing Technology and Its Application
Book Chapter
Because of the characteristics of temporal constraints, there may be a relationship between structural and temporal information in temporal XML. This chapter focuses on this and studies a temporal ...XML index based on linear order. Firstly, it discusses linear order partition, which provides a mathematical framework to the temporal index. Secondly, it researches algorithms on linear order branches, which are crucial in creating the index. Thirdly, it studies the querying paths based on temporal XML. Finally, it implements simulations with large number of testing data whose results indicate that the index is feasible and effective.
