Bayesian Active Summarization

Understand

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.