  To ensure readability, text is often written and presented with due
formatting. These text formatting devices help the writer to effectively convey
the narrative. At the same time, these help the readers pick up the structure
of the discourse and comprehend the conveyed information. There have been a
number of linguistic theories on discourse structure of text. However, these
theories only consider unformatted text. Multimedia text contains rich
formatting features which can be leveraged for various NLP tasks. In this
paper, we study some of these discourse features in multimedia text and what
communicative function they fulfil in the context. We examine how these
multimedia discourse features can be used to improve an information extraction
system. We show that the discourse and text layout features provide information
that is complementary to lexical semantic information commonly used for
information extraction. As a case study, we use these features to harvest
structured subject knowledge of geometry from textbooks. We show that the
harvested structured knowledge can be used to improve an existing solver for
geometry problems, making it more accurate as well as more explainable.
