In order to overcome these issues, we reconsider the task of summarization from a human-centered perspective.
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 explore the notion of uncertainty in the context of modern abstractive summarization models, using the tools of Bayesian Deep Learning.
With this approach we can decompose the problem of long document summarization into smaller and simpler problems, reducing computational complexity and creating more training examples, which at the same time contain less noise in the target summaries compared to the standard approach.
Ranked #13 on Text Summarization on Pubmed (using extra training data)
We propose SUSIE, a novel summarization method that can work with state-of-the-art summarization models in order to produce structured scientific summaries for academic articles.