2021/12/23 by Jongyun Choi, Choi, Jongyun, Hyesoo Kong +7
Computer Science · #Digital Libraries (cs.DL) #FOS: Computer and information sciences #I.2.7 #Image Processing and 3D Reconstruction #Image Retrieval and Classification Techniques #Information Retrieval (cs.IR) #Machine Learning (cs.LG) #Web Data Mining and Analysis
paper · pdf · doi:10.48550/arxiv.2112.12353
openalex publication_date 2021/12/23 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.