  Math expressions are important parts of scientific and educational documents,
but some of them may be challenging for junior scholars or students to
understand. Nevertheless, constructing textual descriptions for math
expressions is nontrivial. In this paper, we explore the feasibility to
automatically construct descriptions for math expressions. But there are two
challenges that need to be addressed: 1) finding relevant documents since a
math equation understanding usually requires several topics, but these topics
are often explained in different documents. 2) the sparsity of the collected
relevant documents making it difficult to extract reasonable descriptions.
Different documents mainly focus on different topics which makes model hard to
extract salient information and organize them to form a description of math
expressions. To address these issues, we propose a hybrid model (MathDes) which
contains two important modules: Selector and Summarizer. In the Selector, a
Topic Relation Graph (TRG) is proposed to obtain the relevant documents which
contain the comprehensive information of math expressions. TRG is a graph built
according to the citations between expressions. In the Summarizer, a
summarization model under the Integer Linear Programming (ILP) framework is
proposed. This module constructs the final description with the help of a
timeline that is extracted from TRG. The experimental results demonstrate that
our methods are promising for this task and outperform the baselines in all
aspects.
