  Information extraction from scholarly articles is a challenging task due to
the sizable document length and implicit information hidden in text, figures,
and citations. Scholarly information extraction has various applications in
exploration, archival, and curation services for digital libraries and
knowledge management systems. We present MORTY, an information extraction
technique that creates structured summaries of text from scholarly articles.
Our approach condenses the article's full-text to property-value pairs as a
segmented text snippet called structured summary. We also present a sizable
scholarly dataset combining structured summaries retrieved from a scholarly
knowledge graph and corresponding publicly available scientific articles, which
we openly publish as a resource for the research community. Our results show
that structured summarization is a suitable approach for targeted information
extraction that complements other commonly used methods such as question
answering and named entity recognition.
