CTRLsum: Towards Generic Controllable Text Summarization

Junxian He, Wojciech Kryscinski, Bryan McCann, Nazneen Rajani, Caiming Xiong


Abstract
Current summarization systems yield generic summaries that are disconnected from users’ preferences and expectations. To address this limitation, we present CTRLsum, a generic framework to control generated summaries through a set of keywords. During training keywords are extracted automatically without requiring additional human annotations. At test time CTRLsum features a control function to map control signal to keywords; through engineering the control function, the same trained model is able to be applied to control summaries on various dimensions, while neither affecting the model training process nor the pretrained models. We additionally explore the combination of keywords and text prompts for more control tasks. Experiments demonstrate the effectiveness of CTRLsum on three domains of summarization datasets and five control tasks: (1) entity-centric and (2) length-controllable summarization, (3) contribution summarization on scientific papers, (4) invention purpose summarization on patent filings, and (5) question-guided summarization on news articles. Moreover, when used in a standard, unconstrained summarization setting, CTRLsum is comparable or better than strong pretrained systems.
Anthology ID:
2022.emnlp-main.396
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
5879–5915
Language:
URL:
https://aclanthology.org/2022.emnlp-main.396
DOI:
10.18653/v1/2022.emnlp-main.396
Bibkey:
Cite (ACL):
Junxian He, Wojciech Kryscinski, Bryan McCann, Nazneen Rajani, and Caiming Xiong. 2022. CTRLsum: Towards Generic Controllable Text Summarization. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 5879–5915, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
CTRLsum: Towards Generic Controllable Text Summarization (He et al., EMNLP 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.emnlp-main.396.pdf