Analysis of Sentence Ordering Based on Support Vector Machine

Abstract

In this paper, we present a practical method of sentence ordering in extractive 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.

Chapter Ordering in Novels

“…Traditionally, this was tackled using human designed features and classical machine learning techniques. This included heuristics with Markov models (Barzilay and Lee, 2004;Bollegala et al, 2005;Ji and Pulman, 2006), K-means clustering (Ji and Nie, 2008;Zhang, 2011), support vector machines (Bollegala et al, 2006;Nahnsen, 2009;Peng et al, 2009;Yanase et al, 2015) and others like latent semantic analysis (Zhang et al, 2010) and conditional random fields (Gella and Duong Thanh, 2012). We also note the representation of sentence ordering as a graph in many past works as well (Elsner and Charniak, 2011;Li et al, 2011;Guinaudeau and Strube, 2013).…”