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

Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, Xuanjing Huang


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. Besides, we find an effective way to improve the current framework and achieve the state-of-the-art result on CNN/DailyMail by a large margin based on our observations and analysis. Hopefully, our work could provide more hints for future research on extractive summarization.
Anthology ID:
P19-1100
Volume:
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2019
Address:
Florence, Italy
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1049–1058
Language:
URL:
https://aclanthology.org/P19-1100
DOI:
10.18653/v1/P19-1100
Bibkey:
Cite (ACL):
Ming Zhong, Pengfei Liu, Danqing Wang, Xipeng Qiu, and Xuanjing Huang. 2019. Searching for Effective Neural Extractive Summarization: What Works and What’s Next. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 1049–1058, Florence, Italy. Association for Computational Linguistics.
Cite (Informal):
Searching for Effective Neural Extractive Summarization: What Works and What’s Next (Zhong et al., ACL 2019)
Copy Citation:
PDF:
https://aclanthology.org/P19-1100.pdf
Video:
 https://vimeo.com/384476489
Code
 additional community code
Data
CNN/Daily MailNEWSROOM