Rhetorical Sentence Classification for Automatic Title Generation in Scientific Article

Jan Wira Gotama Putra, Masayu Leylia Khodra

Abstract


In this paper, we proposed a work onrhetorical corpus construction andsentence classification model experiment that specifically could be incorporated in automatic paper title generation task for scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification model is useful in task which considers document’s discourse structure. We performed experiments using two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run (0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE filter for imbalanced data

Keywords


rhetorical corpus construction, rhetorical classification, automatic title generation, scientific article

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DOI: http://dx.doi.org/10.12928/telkomnika.v15i2.4061

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TELKOMNIKA Telecommunication, Computing, Electronics and Control
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