Efficiency Metrics for Data-Driven Models: A Text Summarization Case Study
This work addresses a methodological gap for researchers in NLP by providing tools to evaluate model efficiency, but it is incremental as it builds on existing models and tasks.
The paper tackled the lack of data efficiency metrics in data-driven models by proposing three new metrics and applying them to text summarization and title generation tasks, revealing that the Transformer model is the most efficient, though no concrete numbers are provided for its performance.
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.