Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study

Erion Çano, Ondřej Bojar


Abstract
Using data-driven models for solving text summarization or similar tasks has become very common in the last years. Yet most of the studies report basic accuracy scores only, and nothing is known about the ability of the proposed models to improve when trained on more data. In this paper, we define and propose three data efficiency metrics: data score efficiency, data time deficiency and overall data efficiency. We also propose a simple scheme that uses those metrics and apply it for a more comprehensive evaluation of popular methods on text summarization and title generation tasks. For the latter task, we process and release a huge collection of 35 million abstract-title pairs from scientific articles. Our results reveal that among the tested models, the Transformer is the most efficient on both tasks.
Anthology ID:
W19-8630
Volume:
Proceedings of the 12th International Conference on Natural Language Generation
Month:
October–November
Year:
2019
Address:
Tokyo, Japan
Editors:
Kees van Deemter, Chenghua Lin, Hiroya Takamura
Venue:
INLG
SIG:
SIGGEN
Publisher:
Association for Computational Linguistics
Note:
Pages:
229–239
Language:
URL:
https://aclanthology.org/W19-8630
DOI:
10.18653/v1/W19-8630
Bibkey:
Cite (ACL):
Erion Çano and Ondřej Bojar. 2019. Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study. In Proceedings of the 12th International Conference on Natural Language Generation, pages 229–239, Tokyo, Japan. Association for Computational Linguistics.
Cite (Informal):
Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study (Çano & Bojar, INLG 2019)
Copy Citation:
PDF:
https://aclanthology.org/W19-8630.pdf