TY - EJOU AU - Choi, Jongyun AU - Kong, Hyesoo AU - Yoon, Hwamook AU - Oh, Heungseon AU - Jung, Yuchul TI - LAME: Layout-Aware Metadata Extraction Approach for Research Articles T2 - Computers, Materials \& Continua PY - 2022 VL - 72 IS - 2 SN - 1546-2226 AB - 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. KW - Automatic layout analysis; layout-MetaBERT; metadata extraction; research article DO - 10.32604/cmc.2022.025711