Publication Type
Conference Proceeding Article
Version
publishedVersion
Publication Date
7-2015
Abstract
We explore using relevant tweets of a given news article to help sentence compression for generating compressive news highlights. We extend an unsupervised dependency-tree based sentence compression approach by incorporating tweet information to weight the tree edge in terms of informativeness and syntactic importance. The experimental results on a public corpus that contains both news articles and relevant tweets show that our proposed tweets guided sentence compression method can improve the summarization performance significantly compared to the baseline generic sentence compression method.
Discipline
Databases and Information Systems
Research Areas
Data Science and Engineering
Publication
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015)
First Page
50
Last Page
56
Identifier
10.3115/v1/P15-2009
Publisher
Association for Computational Linguistics
City or Country
Beijing, China
Citation
WEI, Zhongyu; LIU, Yang; LI, Chen; and GAO, Wei.
Using tweets to help sentence compression for news highlights generation. (2015). Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL 2015). 50-56.
Available at: https://ink.library.smu.edu.sg/sis_research/4575
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.3115/v1/P15-2009