Rhetorical Sentence Classification for Automatic Title Generation in Scientific Article

Jan Wira Gotama Putra, Masayu Leylia Khodra


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


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

Full Text:


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

Article Metrics

Abstract view : 189 times
PDF - 129 times


  • There are currently no refbacks.

Copyright (c) 2017 Universitas Ahmad Dahlan

TELKOMNIKA Telecommunication, Computing, Electronics and Control
ISSN: 1693-6930, e-ISSN: 2302-9293
Universitas Ahmad Dahlan, 4th Campus, 9th Floor, LPPI Room
Jl. Ringroad Selatan, Kragilan, Tamanan, Banguntapan, Bantul, Yogyakarta, Indonesia 55191
Phone: +62 (274) 563515, 511830, 379418, 371120 ext. 4902, Fax: +62 274 564604

Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.