HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization

Shuyang Cao, Lu Wang


Abstract
Document structure is critical for efficient information consumption. However, it is challenging to encode it efficiently into the modern Transformer architecture. In this work, we present HIBRIDS, which injects Hierarchical Biases foR Incorporating Document Structure into attention score calculation. We further present a new task, hierarchical question-summary generation, for summarizing salient content in the source document into a hierarchy of questions and summaries, where each follow-up question inquires about the content of its parent question-summary pair. We also annotate a new dataset with 6,153 question-summary hierarchies labeled on government reports. Experiment results show that our model produces better question-summary hierarchies than comparisons on both hierarchy quality and content coverage, a finding also echoed by human judges. Additionally, our model improves the generation of long-form summaries from long government reports and Wikipedia articles, as measured by ROUGE scores.
Anthology ID:
2022.acl-long.58
Volume:
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
786–807
Language:
URL:
https://aclanthology.org/2022.acl-long.58
DOI:
10.18653/v1/2022.acl-long.58
Bibkey:
Cite (ACL):
Shuyang Cao and Lu Wang. 2022. HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 786–807, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
HIBRIDS: Attention with Hierarchical Biases for Structure-aware Long Document Summarization (Cao & Wang, ACL 2022)
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PDF:
https://aclanthology.org/2022.acl-long.58.pdf