LAME: Layout Aware Metadata Extraction Approach for Research Articles
This addresses the problem of metadata extraction for researchers and librarians dealing with varied journal layouts, representing a strong specific gain rather than a broad breakthrough.
The paper tackles the challenge of extracting metadata from academic articles with diverse layout formats by proposing the LAME framework, which includes automatic layout analysis, a large training dataset, and Layout-MetaBERT, achieving a Macro-F1 score of 93.27% on unseen journals.
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.