A Study for Sentence Ordering Based on Grey Model
ABSTRACT 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.
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ABSTRACT: In this paper, we propose a cluster-adjacency based method to order sentences for m ulti-document summarization tasks. Given a group of sentences to be organized into a summary, each sentence was mapped to a theme in source documents by a semi-supervised classification method, and adjacency of pairs of sentences is learned from source documents based on adjacency of clusters they belong to. Then the ordering of the summary sentences can be derived with the first sentence determined. Experiments and evaluations on DUC04 data show that this method gets better performance than other existing sentence ordering methods.
- IEEE Transactions on Neural Networks 02/1997; 8(6):1564. DOI:10.1109/TNN.1997.641482 · 2.95 Impact Factor
- Second 01/2006; Springer.