Searching for Effective Neural Extractive Summarization: What Works and What's Next

Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, Xuanjing Huang
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics  
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
more » ... sult 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 1 .
doi:10.18653/v1/p19-1100 dblp:conf/acl/ZhongLWQH19 fatcat:rldnxmzjfrhs3bjjer7vpqvd5y