Systematically Exploring Redundancy Reduction in Summarizing Long Documents

Wen Xiao, Giuseppe Carenini


Abstract
Our analysis of large summarization datasets indicates that redundancy is a very serious problem when summarizing long documents. Yet, redundancy reduction has not been thoroughly investigated in neural summarization. In this work, we systematically explore and compare different ways to deal with redundancy when summarizing long documents. Specifically, we organize existing methods into categories based on when and how the redundancy is considered. Then, in the context of these categories, we propose three additional methods balancing non-redundancy and importance in a general and flexible way. In a series of experiments, we show that our proposed methods achieve the state-of-the-art with respect to ROUGE scores on two scientific paper datasets, Pubmed and arXiv, while reducing redundancy significantly.
Anthology ID:
2020.aacl-main.51
Volume:
Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing
Month:
December
Year:
2020
Address:
Suzhou, China
Editors:
Kam-Fai Wong, Kevin Knight, Hua Wu
Venue:
AACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
516–528
Language:
URL:
https://aclanthology.org/2020.aacl-main.51
DOI:
Bibkey:
Cite (ACL):
Wen Xiao and Giuseppe Carenini. 2020. Systematically Exploring Redundancy Reduction in Summarizing Long Documents. In Proceedings of the 1st Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 10th International Joint Conference on Natural Language Processing, pages 516–528, Suzhou, China. Association for Computational Linguistics.
Cite (Informal):
Systematically Exploring Redundancy Reduction in Summarizing Long Documents (Xiao & Carenini, AACL 2020)
Copy Citation:
PDF:
https://aclanthology.org/2020.aacl-main.51.pdf
Code
 Wendy-Xiao/redundancy_reduction_longdoc
Data
Arxiv HEP-TH citation graphCNN/Daily MailPubmed