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.
[Show abstract][Hide abstract] 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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.