Bayesian Active Summarization
This work addresses the challenge of leveraging large summarization models with limited annotated data, which is incremental as it adapts existing active learning methods to a new domain.
The paper tackles the problem of applying active learning to text summarization, introducing Bayesian Active Summarization (BAS) which achieves better and more robust performance than random selection, especially for small annotation budgets.
Bayesian Active Learning has had significant impact to various NLP problems, but nevertheless it's application to text summarization has been explored very little. We introduce Bayesian Active Summarization (BAS), as a method of combining active learning methods with state-of-the-art summarization models. Our findings suggest that BAS achieves better and more robust performance, compared to random selection, particularly for small and very small data annotation budgets. Using BAS we showcase it is possible to leverage large summarization models to effectively solve real-world problems with very limited annotated data.