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LAME: Layout-Aware Metadata Extraction Approach for Research Articles

Jongyun Choi1, Hyesoo Kong2, Hwamook Yoon2, Heungseon Oh3, Yuchul Jung1,*
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.

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


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.


Automatic layout analysis; layout-MetaBERT; metadata extraction; research article

Cite This Article

J. Choi, H. Kong, H. Yoon, H. Oh and Y. Jung, "Lame: layout-aware metadata extraction approach for research articles," Computers, Materials & Continua, vol. 72, no.2, pp. 4019–4037, 2022.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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