Title: New Methods for Metadata Extraction from Scientific Literature,
Abstract: Within the past few decades we have witnessed digital revolution, which moved
scholarly communication to electronic media and also resulted in a substantial
increase in its volume. Nowadays keeping track with the latest scientific
achievements poses a major challenge for the researchers. Scientific
information overload is a severe problem that slows down scholarly
communication and knowledge propagation across the academia. Modern research
infrastructures facilitate studying scientific literature by providing
intelligent search tools, proposing similar and related documents, visualizing
citation and author networks, assessing the quality and impact of the articles,
and so on. In order to provide such high quality services the system requires
the access not only to the text content of stored documents, but also to their
machine-readable metadata. Since in practice good quality metadata is not
always available, there is a strong demand for a reliable automatic method of
extracting machine-readable metadata directly from source documents. This
research addresses these problems by proposing an automatic, accurate and
flexible algorithm for extracting wide range of metadata directly from
scientific articles in born-digital form. Extracted information includes basic
document metadata, structured full text and bibliography section. Designed as a
universal solution, proposed algorithm is able to handle a vast variety of
publication layouts with high precision and thus is well-suited for analyzing
heterogeneous document collections. This was achieved by employing supervised
and unsupervised machine-learning algorithms trained on large, diverse
datasets. The evaluation we conducted showed good performance of proposed
metadata extraction algorithm. The comparison with other similar solutions also
proved our algorithm performs better than competition for most metadata types.