GSum: A General Framework for Guided Neural Abstractive Summarization

Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, Graham Neubig


Abstract
Neural abstractive summarization models are flexible and can produce coherent summaries, but they are sometimes unfaithful and can be difficult to control. While previous studies attempt to provide different types of guidance to control the output and increase faithfulness, it is not clear how these strategies compare and contrast to each other. In this paper, we propose a general and extensible guided summarization framework (GSum) that can effectively take different kinds of external guidance as input, and we perform experiments across several different varieties. Experiments demonstrate that this model is effective, achieving state-of-the-art performance according to ROUGE on 4 popular summarization datasets when using highlighted sentences as guidance. In addition, we show that our guided model can generate more faithful summaries and demonstrate how different types of guidance generate qualitatively different summaries, lending a degree of controllability to the learned models.
Anthology ID:
2021.naacl-main.384
Volume:
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2021
Address:
Online
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4830–4842
Language:
URL:
https://aclanthology.org/2021.naacl-main.384
DOI:
10.18653/v1/2021.naacl-main.384
Bibkey:
Cite (ACL):
Zi-Yi Dou, Pengfei Liu, Hiroaki Hayashi, Zhengbao Jiang, and Graham Neubig. 2021. GSum: A General Framework for Guided Neural Abstractive Summarization. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4830–4842, Online. Association for Computational Linguistics.
Cite (Informal):
GSum: A General Framework for Guided Neural Abstractive Summarization (Dou et al., NAACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.naacl-main.384.pdf
Code
 neulab/guided_summarization
Data
CNN/Daily MailWikiHow