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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/N16-1008.pdf