Abstract
Automatic document summarization is always attractive to computer science researchers. A novel approach is proposed to address this topic and mainly focuses on the summarization of plain documents. Conventional summarization methods do not fully use the inter-sentence relevance that is not preserved during the processing. In contrast, to tackle the problem and incorporate the latent relations among sentences, our approach constructs relevance structures at sentence-level for plain documents and each sentence is scored with a significance value. Accordingly, important sentences “present” themselves automatically, and the summary paragraph is then generated by selecting top-k scored sentences. Convergence of the algorithm is proved, and experiment, which is conducted on two data sets (DUC 2006 and DUC 2007), shows that the proposed model gives convincing results.
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Li, X., Zhu, S., Xie, H., Li, Q. (2013). Document Summarization via Self-Present Sentence Relevance Model. In: Meng, W., Feng, L., Bressan, S., Winiwarter, W., Song, W. (eds) Database Systems for Advanced Applications. DASFAA 2013. Lecture Notes in Computer Science, vol 7826. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37450-0_24
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DOI: https://doi.org/10.1007/978-3-642-37450-0_24
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