N. Adilah Hanin Zahri¹, Fumiyo Fukumoto², Matsyoshi Suguru² and Ong Bi Lynn¹, ¹University of Malaysia Perlis, Malaysia and ²University of Yamanashi, Japan

Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations that is useful to retrieve sentences with identical content and performed the identification of those relations using SVMs. By exploiting the rhetorical relations exist between sentences, we generate clusters of similar sentences from document sets. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of candidates summary. We evaluated our method by measuring the cohesion and separation of the clusters and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in cluster-based text summarization.

Rhetorical Relations, Text Clustering, Extractive Text Summarization, Support Vector Machine, Probability Model, Markov Random Walk Model