FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications

Abstract

The unprecedented growth of the research publications in diversified domains has overwhelmed the research community. It requires a cumbersome process to extract this enormous information by manually analyzing these research documents. To automatically extract content of a document in a structured way, metadata and content must be annotated. Scientific community has been focusing on automatic extraction of content by forming different heuristics and applying different machine learning techniques. One of the renowned conference organizers, ESWC organizes state-of-the-art challenge to extract metadata like authors, affiliations, countries in affiliations, supplementary material, sections, table, figures, funding agencies, and EU funded projects from PDF files of research articles. We have proposed a feature centric technique that can be used to extract logical layout structure of articles from publishers with diversified composition styles. To extract unique metadata from a research article placed in logical layout structure, we have developed a four-staged novel approach “FLAG-PDFe”. The approach is built upon distinct and generic features based on the textual and the geometric information from the raw content of research documents. At the first stage, the distinct features are used to identify different physical layout components of an individual article. Since research journals follow their unique publishing styles and layout formats, therefore, we develop generic features to handle these diversified publishing patterns. We employ support vector classification (SVC) in the third stage to extract the logical layout structure (LLS)/ sections of an article, after performing comprehensive evaluation of generic features and machine learning models. Finally, we further apply heuristics on LLS to extract the desired metadata of an article. The outcomes of the study are obtained using the gold standard data set. The results yields 0.877 recall, precision 0.928 and 0.897 F-measure. Our approach has achieved a 16% gain on f-measure when compared to the best approach of the ESWC challenge.