Rhetorical Sentence Classification for Automatic Title Generation in Scientific Article

Abstract

In this paper, we proposed a work on rhetorical corpus construction and sentence 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.

Automatic Title Generation in Scientific Articles for Authorship Assistance: A Summarization Approach

“…Previous researchers have done sentence classification as supervised learning task [12,[14][15][16][17]. In this study, of particular interest was rhetorical sentence classification where sentences are classified according to their information type/communicative purpose to judge their importance [12,18].…”

“…Seaghdha and Teufel [19] argued that words and linguistic forms in scientific writing are not unique to the research topic, while the writing structure can have different inter-domains but a similar intradomain. This means that the text structure of scientific articles from the same domain can be different with that from another domain and hence capturing the writing patterns of each domain is useful for building a domain-specific classifier [12]. Table 1 Rhetorical categories [12].…”

“…This means that the text structure of scientific articles from the same domain can be different with that from another domain and hence capturing the writing patterns of each domain is useful for building a domain-specific classifier [12]. Table 1 Rhetorical categories [12].…”

“…Statement of the specific research goal or hypothesis of the paper OWN_MTHD New knowledge claim or proposed method NR Other information that does not belong to AIM or OWN_MTHD Fifteen different rhetorical categories were heuristically tailored into three categories for the purpose of title generation [12], as shown in Table 1. This annotation scheme was used to build sentence classifiers separately for computational linguistics and chemistry scientific articles.…”

“…Sentences, i.e. the textual units that are analyzed, are classified into one of three rhetorical categories: AIM (research goal), OWN_MTHD (research method), and NR (not relevant) [12]. The proposed system generates several title candidates based on processing a paper's abstract.…”