Hierarchical Attention Graph for Scientific Document Summarization in Global and Local Level

Understand

Scientific document summarization has been a challenging task due to the long structure of the input text.

• The long input hinders the simultaneous effective modeling of both global high-order relations between sentences and local intra-sentence relations which is the most critical step in extractive summarization.
• However, existing methods mostly focus on one type of relation, neglecting the simultaneous effective modeling of both relations, which can lead to insufficient learning of semantic representations.
• In this paper, we propose HAESum, a novel approach utilizing graph neural networks to locally and globally model documents based on their hierarchical discourse structure.