LAME: Layout-Aware Metadata Extraction Approach for Research Articles

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.

Annotated Open Corpus Construction and BERT-Based Approach for Automatic Metadata Extraction From Korean Academic Papers

“…Recently, a research using the BERT model for automatic metadata extraction from Korean papers have been conducted. The authors of [7] proposed a metadata extraction method using Layout-MetaBERT, which was developed by pre-training the BERT model with metadata layout information of papers. However, in this research, training data from 70 types of Korean academic journals were used, which is a limited amount to cover overall journals.…”

“…However, in this research, training data from 70 types of Korean academic journals were used, which is a limited amount to cover overall journals. Unlike the work of [7], we constructed the training data from approximately 500 types of Korean academic journals, so that a new model using our data has higher coverage that can extract metadata from Korean papers in more various formats of journal. In this paper, we developed new models, KorSciBERT-ME-J and KorSciBERT-ME-J+C, by applying the concept of learning auxiliary sentences referring to [27].…”

“…The training data were collected from 10 types of Korean academic journals and an automatic extraction model for 9 metadata fields was developed. In [7], the metadata layouts of Korean papers were extracted and a layout pretraining model, Layout-MetaBERT, was developed, which could perform automatic metadata extraction. Training data for 70 types of Korean academic journals were constructed and an automatic extraction model for 10 metadata fields was proposed.…”

“…Currently, over 500 types of Korean academic journals are being collected by the KISTI. However, the training data obtained from the two prior studies [6], [7] are not sufficient to cover this number of journal formats. No other studies relating to metadata extraction from Korean papers have been conducted, mainly because of lack of large amounts of high-quality training data that is required to follow the latest research trends using deep learning-based models.…”