A hybrid approach to discover semantic hierarchical sections in scholarly documents

Scholarly documents are usually composed of sections, each of which serves a different purpose by conveying specific context. The ability to automatically identify sections would allow us to understand the semantics of what is different in different sections of documents, such as what was in the introduction, methodologies used, experimental types, trends, etc. We propose a set of hybrid algorithms to 1) automatically identify section boundaries, 2) recognize standard sections, and 3) build a hierarchy of sections. Our algorithms achieve an F-measure of 92.38% in section boundary detection, 96% accuracy (average) on standard section recognition, and 95.51% in accuracy in the section positioning task.