Open Access

ARTICLE

LAME: Layout-Aware Metadata Extraction Approach for Research Articles

Jongyun Choi¹, Hyesoo Kong², Hwamook Yoon², Heungseon Oh³, Yuchul Jung^1,*

1 Department of Computer Engineering, Kumoh National Institute of Technology (KIT), Gumi, Korea
2 Korea Institute of Science and Technology Information (KISTI), Daejeon, Korea
3 School of Computer Science and Engineering, Korea University of Technology and Education (KOREATECH), Cheonan, Korea

* Corresponding Author: Yuchul Jung. Email:

Computers, Materials & Continua 2022, 72(2), 4019-4037. https://doi.org/10.32604/cmc.2022.025711

Received 02 December 2021; Accepted 27 January 2022; Issue published 29 March 2022

Abstract

The volume of academic literature, such as academic conference papers and journals, has increased rapidly worldwide, and research on metadata extraction is ongoing. However, high-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers. To accommodate the diversity of the layouts of academic journals, we propose a novel LAyout-aware Metadata Extraction (LAME) framework equipped with the three characteristics (e.g., design of automatic layout analysis, construction of a large meta-data training set, and implementation of metadata extractor). In the framework, we designed an automatic layout analysis using PDFMiner. Based on the layout analysis, a large volume of metadata-separated training data, including the title, abstract, author name, author affiliated organization, and keywords, were automatically extracted. Moreover, we constructed a pre-trained model, Layout-MetaBERT, to extract the metadata from academic journals with varying layout formats. The experimental results with our metadata extractor exhibited robust performance (Macro-F1, 93.27%) in metadata extraction for unseen journals with different layout formats.

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Keywords

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