MReD: A Meta-Review Dataset for Structure-Controllable Text Generation

Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, Luo Si


Abstract
When directly using existing text generation datasets for controllable generation, we are facing the problem of not having the domain knowledge and thus the aspects that could be controlled are limited. A typical example is when using CNN/Daily Mail dataset for controllable text summarization, there is no guided information on the emphasis of summary sentences. A more useful text generator should leverage both the input text and the control signal to guide the generation, which can only be built with deep understanding of the domain knowledge. Motivated by this vision, our paper introduces a new text generation dataset, named MReD. Our new dataset consists of 7,089 meta-reviews and all its 45k meta-review sentences are manually annotated with one of the 9 carefully defined categories, including abstract, strength, decision, etc. We present experimental results on start-of-the-art summarization models, and propose methods for structure-controlled generation with both extractive and abstractive models using our annotated data. By exploring various settings and analyzing the model behavior with respect to the control signal, we demonstrate the challenges of our proposed task and the values of our dataset MReD. Meanwhile, MReD also allows us to have a better understanding of the meta-review domain.
Anthology ID:
2022.findings-acl.198
Volume:
Findings of the Association for Computational Linguistics: ACL 2022
Month:
May
Year:
2022
Address:
Dublin, Ireland
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2521–2535
Language:
URL:
https://aclanthology.org/2022.findings-acl.198
DOI:
10.18653/v1/2022.findings-acl.198
Bibkey:
Cite (ACL):
Chenhui Shen, Liying Cheng, Ran Zhou, Lidong Bing, Yang You, and Luo Si. 2022. MReD: A Meta-Review Dataset for Structure-Controllable Text Generation. In Findings of the Association for Computational Linguistics: ACL 2022, pages 2521–2535, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
MReD: A Meta-Review Dataset for Structure-Controllable Text Generation (Shen et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-acl.198.pdf
Code
 shen-chenhui/mred
Data
CNN/Daily Mail