Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness

Yun-Zhu Song, Yi-Syuan Chen, Hong-Han Shuai


Abstract
A notable challenge in Multi-Document Summarization (MDS) is the extremely-long length of the input. In this paper, we present an extract-then-abstract Transformer framework to overcome the problem. Specifically, we leverage pre-trained language models to construct a hierarchical extractor for salient sentence selection across documents and an abstractor for rewriting the selected contents as summaries. However, learning such a framework is challenging since the optimal contents for the abstractor are generally unknown. Previous works typically create pseudo extraction oracle to enable the supervised learning for both the extractor and the abstractor. Nevertheless, we argue that the performance of such methods could be restricted due to the insufficient information for prediction and inconsistent objectives between training and testing. To this end, we propose a loss weighting mechanism that makes the model aware of the unequal importance for the sentences not in the pseudo extraction oracle, and leverage the fine-tuned abstractor to generate summary references as auxiliary signals for learning the extractor. Moreover, we propose a reinforcement learning method that can efficiently apply to the extractor for harmonizing the optimization between training and testing. Experiment results show that our framework substantially outperforms strong baselines with comparable model sizes and achieves the best results on the Multi-News, Multi-XScience, and WikiCatSum corpora.
Anthology ID:
2022.naacl-main.120
Volume:
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
July
Year:
2022
Address:
Seattle, United States
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1667–1681
Language:
URL:
https://aclanthology.org/2022.naacl-main.120
DOI:
10.18653/v1/2022.naacl-main.120
Bibkey:
Cite (ACL):
Yun-Zhu Song, Yi-Syuan Chen, and Hong-Han Shuai. 2022. Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1667–1681, Seattle, United States. Association for Computational Linguistics.
Cite (Informal):
Improving Multi-Document Summarization through Referenced Flexible Extraction with Credit-Awareness (Song et al., NAACL 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.naacl-main.120.pdf
Code
 yunzhusong/NAACL2022-REFLECT
Data
Multi-NewsMulti-XScienceWikiCatSum