Improving Extractive Summaries through Abstractive Transformers

Abstract

This paper focuses on using a novel unsupervised summarization layer, called BART-textrank, that uses both abstractive and extractive techniques to produce a final extractive summary. This unsupervised layer is versatile since it can be added on top of any abstractive summarizer without additional training. It is used in conjunction with a base size transformer that achieves SOTA performance in a few metrics when comparing to transformer methods with similar parameter size, and remains competitive with large transformers. The competitiveness of BART-textrank is apparent in figure 4.2.