login
Home / Papers / Efficient Attentions for Long Document Summarization

Efficient Attentions for Long Document Summarization

125 Citations2021
Luyang Huang, Shuyang Cao, Nikolaus Nova Parulian
journal unavailable

Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source is proposed, able to process ten times more tokens than existing models that use full attentions.

Abstract

Luyang Huang, Shuyang Cao, Nikolaus Parulian, Heng Ji, Lu Wang. Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2021.