Rhetorical Sentences Classification Based on Section Class and Title of Paper for Experimental Technical Papers


  • Afrida Helen Department of Informatics, School of Electrical Engineering and Informatics (STEI) Bandung Institute of Technology, Jalan Ganesha 10, Bandung, 40132
  • Ayu Purwarianti Department of Informatics, School of Electrical Engineering and Informatics (STEI) Bandung Institute of Technology, Jalan Ganesha 10, Bandung, 40132
  • Dwi H. Widyantoro Department of Informatics, School of Electrical Engineering and Informatics (STEI) Bandung Institute of Technology, Jalan Ganesha 10, Bandung, 40132




Rhetorical sentence classification is an interesting approach for making extractive summaries but this technique still needs to be developed because the performance of automatic rhetorical sentence classification is still poor. Rhetorical sentences are sentences that contain rhetorical words or phrases. Rhetorical sentences not only appear in the contents of a paper but also in the title. In this study, features related to section class and title class that have been proposed in a previous research were further developed. Our method uses different techniques to reach automatic section class extraction for which we introduce new, format-based features. Furthermore, we propose automatic rhetoric phrase extraction from the title. The corpus we used was a collection of technical-experimental scientific papers. Our method uses the Support Vector Machine (SVM) algorithm and the Naïve Bayesian algorithm for classification. The four categories used were: Problem, Method, Data, and Result. It was hypothesized that these features would be able to improve classification accuracy compared to previous methods. The F-measure for these categories reached up to 14%. 


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