Detecting key sentences for automatic assistance in peer reviewing research articles in educational sciences

Abstract

The evaluation of scientific performance is gaining importance in all research disciplines. The basic process of the evaluation is peer reviewing, which is a time-consuming activity. In order to facilitate and speed up peer reviewing processes we have developed an exploratory NLP system in the field of educational sciences. The system highlights key sentences, which are supposed to reflect the most important threads of the article The highlighted sentences offer guidance on the content-level while structural elements - the title, abstract, keywords, section headings - give an orientation about the design of the argumentation in the article. The system is implemented using a discourse analysis module called concept matching applied on top of the Xerox Incremental Parser, a rule-based dependency parser. The first results are promising and indicate the directions for the future development of the system.