Constructing a high-quality dataset for automated creation of summaries of fundamental contributions of research articles.

Autor/inn/en	Chen, Haihua; Nguyen, Huyen; Alghamdi, Asmaa
Titel	Constructing a high-quality dataset for automated creation of summaries of fundamental contributions of research articles.
Quelle	In: Scientometrics, (2022) 12, S.7061-7075 PDF als Volltext Verfügbarkeit
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0138-9130
DOI	10.1007/s11192-022-04380-z
Schlagwörter	Academic literature; Research contribution; Dataset; Text classification; Machine learning; BERT
Abstract	Abstract Research contributions, which indicate how a research paper contributes new knowledge or new understanding in contrast to prior research on the topic, are the most valuable type of information for researchers to understand the main content of a paper. However, there is little research using research contributions to identify and recommend valuable knowledge in academic literature for users. Instead, most existing studies mainly focus on the analysis of other elements in academic literature, such as keywords, citations, rhetorical structure, discourse, and others. This paper first introduces a fine-grained annotation scheme with six categories for research contributions in academic literature. To evaluate the reliability of our annotation scheme, we conduct annotation on 5024 sentences collected from Proceedings of the Annual Meeting of the Association for Computational Linguistics (ACL Anthology) and an academic journal Information Processing & Management (IP &M). We reach an inter-annotator agreement of Cohen’s kappa = 0.91 and Fleiss’ kappa = 0.91, demonstrating the high quality of the dataset. We then built two types of classifiers for automated research contribution identification based on the dataset: classic feature-based machine learning (ML) and transformer-based deep learning (DL). Our experimental results show that SCI-BERT, a pretrained language model for scientific text, achieves the best performance with an F1 score of 0.58, improving the best classic ML model (nouns + verbs + tf-idf + random forest) by 2%. This also indicates a comparable power of classic feature-based ML models to DL-based model like SCI-BERT on this dataset. The fine-grained annotation scheme can be applied for large-scale analysis for research contributions in academic literature. The automated research contribution classifiers built in this paper provide the basis for the automatic research contributions extraction and knowledge fragment recommendation. The high-quality research contribution dataset developed in this research is publicly available on Zenodo https://zenodo.org/record/6284137#.YhkZ7-iZO4Q. The code for the data analysis and experiments will be released at: https://github.com/HuyenNguyenHelen/Contribution-Sentence-Classification.
Erfasst von	OLC
Update	2023/2/05