LAME: Layout Aware Metadata Extraction Approach for Research Articles

• URL: http://arxiv.org/abs/2112.12353v1
• Date: Thu, 23 Dec 2021 04:23:08 GMT
• Title: LAME: Layout Aware Metadata Extraction Approach for Research Articles
• Authors: Jongyun Choi, Hyesoo Kong, Hwamook Yoon, Heung-Seon Oh, Yuchul Jung
• Abstract summary: The volume of academic literature, such as academic conference papers and journals, has increased rapidly worldwide. High-performing metadata extraction is still challenging due to diverse layout formats according to journal publishers. We propose a novel LAyout-aware Metadata Extraction framework equipped with the three characteristics.
• Score: 1.8899300124593648
• License: http://creativecommons.org/licenses/by/4.0/
• 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 an automatic layout analysis, construction of a large meta-data training set, and construction of Layout-MetaBERT). 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 Layout-MetaBERT to extract the metadata from academic journals with varying layout formats. The experimental results with Layout-MetaBERT exhibited robust performance (Macro-F1, 93.27%) in metadata extraction for unseen journals with different layout formats.

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