SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

Jesse Vig, Wojciech Kryscinski, Karan Goel, Nazneen Rajani


Abstract
Novel neural architectures, training strategies, and the availability of large-scale corpora haven been the driving force behind recent progress in abstractive text summarization. However, due to the black-box nature of neural models, uninformative evaluation metrics, and scarce tooling for model and data analysis the true performance and failure modes of summarization models remain largely unknown. To address this limitation, we introduce SummVis, an open-source tool for visualizing abstractive summaries that enables fine-grained analysis of the models, data, and evaluation metrics associated with text summarization. Through its lexical and semantic visualizations, the tools offers an easy entry point for in-depth model prediction exploration across important dimensions such as factual consistency or abstractiveness. The tool together with several pre-computed model outputs is available at https://summvis.com.
Anthology ID:
2021.acl-demo.18
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations
Month:
August
Year:
2021
Address:
Online
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
150–158
Language:
URL:
https://aclanthology.org/2021.acl-demo.18
DOI:
10.18653/v1/2021.acl-demo.18
Bibkey:
Cite (ACL):
Jesse Vig, Wojciech Kryscinski, Karan Goel, and Nazneen Rajani. 2021. SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations, pages 150–158, Online. Association for Computational Linguistics.
Cite (Informal):
SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization (Vig et al., ACL 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-demo.18.pdf
Video:
 https://aclanthology.org/2021.acl-demo.18.mp4
Code
 robustness-gym/summvis
Data
NEWSROOM