LAME: Layout-Aware Metadata Extraction Approach for Research Articlesopen access
- Authors
- Choi, Jongyun; Kong, Hyesoo; Yoon, Hwamook; Oh, Heungseon; Jung, Yuchul
- Issue Date
- Mar-2022
- Publisher
- TECH SCIENCE PRESS
- Keywords
- Automatic layout analysis; layout-MetaBERT; metadata extrac-tion; research article
- Citation
- CMC-COMPUTERS MATERIALS & CONTINUA, v.72, no.2, pp.4019 - 4037
- Journal Title
- CMC-COMPUTERS MATERIALS & CONTINUA
- Volume
- 72
- Number
- 2
- Start Page
- 4019
- End Page
- 4037
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21096
- DOI
- 10.32604/cmc.2022.025711
- ISSN
- 1546-2218
- 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 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.
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