  Mathematical equations are an important part of dissemination and
communication of scientific information. Students, however, often feel
challenged in reading and understanding math content and equations. With the
development of the Web, students are posting their math questions online.
Nevertheless, constructing a concise math headline that gives a good
description of the posted detailed math question is nontrivial. In this study,
we explore a novel summarization task denoted as geNerating A concise Math
hEadline from a detailed math question (NAME). Compared to conventional
summarization tasks, this task has two extra and essential constraints: 1)
Detailed math questions consist of text and math equations which require a
unified framework to jointly model textual and mathematical information; 2)
Unlike text, math equations contain semantic and structural features, and both
of them should be captured together. To address these issues, we propose
MathSum, a novel summarization model which utilizes a pointer mechanism
combined with a multi-head attention mechanism for mathematical representation
augmentation. The pointer mechanism can either copy textual tokens or math
tokens from source questions in order to generate math headlines. The
multi-head attention mechanism is designed to enrich the representation of math
equations by modeling and integrating both its semantic and structural
features. For evaluation, we collect and make available two sets of real-world
detailed math questions along with human-written math headlines, namely
EXEQ-300k and OFEQ-10k. Experimental results demonstrate that our model
(MathSum) significantly outperforms state-of-the-art models for both the
EXEQ-300k and OFEQ-10k datasets.
