  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.
