Searching for Effective Neural Extractive Summarization:
What Works and What’s Next
Abstract
The recent years have seen remarkable success in the use of deep neural networks on text
summarization. However, there is no clear understanding of why they perform so well, or
how they might be improved. In this paper, we
seek to better understand how neural extractive
summarization systems could benefit from different types of model architectures, transferable knowledge and learning schemas. Additionally, we find an effective way to improve
current frameworks and achieve the state-ofthe-art result on CNN/DailyMail by a large
margin based on our observations and analyses. Hopefully, our work could provide more
clues for future research on extractive summarization. Source code will be available on
Github