MORTY: Structured Summarization for Targeted Information Extraction from Scholarly Articles

TL;DR

MORTY is a novel method for creating structured summaries from scholarly articles, enabling targeted information extraction by condensing full texts into property-value pairs, supported by a new dataset for research.

Contribution

Introduction of MORTY, a structured summarization technique for scholarly articles, and a large dataset combining summaries with scientific articles for research use.

Findings

Structured summaries effectively extract targeted information.

MORTY complements question answering and named entity recognition.

The dataset supports further research in scholarly information extraction.

Abstract

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.