A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization

William Yang Wang, Yashar Mehdad, Dragomir R. Radev, Amanda Stent


Anthology ID:
N16-1008
Volume:
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Month:
June
Year:
2016
Address:
San Diego, California
Editors:
Kevin Knight, Ani Nenkova, Owen Rambow
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
58–68
Language:
URL:
https://aclanthology.org/N16-1008
DOI:
10.18653/v1/N16-1008
Bibkey:
Cite (ACL):
William Yang Wang, Yashar Mehdad, Dragomir R. Radev, and Amanda Stent. 2016. A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization. In Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 58–68, San Diego, California. Association for Computational Linguistics.
Cite (Informal):
A Low-Rank Approximation Approach to Learning Joint Embeddings of News Stories and Images for Timeline Summarization (Wang et al., NAACL 2016)
Copy Citation:
PDF:
https://aclanthology.org/N16-1008.pdf