Abstract
Supervised learning methods and LDA based topic model have been successfully applied in the field of multi-document summarization. In this paper, we propose a novel supervised approach that can incorporate rich sentence features into Bayesian topic models in a principled way, thus taking advantages of both topic model and feature based supervised learning methods. Experimental results on DUC2007, TAC2008 and TAC2009 demonstrate the effectiveness of our approach.- Anthology ID:
- Q13-1008
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 1
- Month:
- Year:
- 2013
- Address:
- Cambridge, MA
- Editors:
- Dekang Lin, Michael Collins
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 89–98
- Language:
- URL:
- https://aclanthology.org/Q13-1008
- DOI:
- 10.1162/tacl_a_00212
- Cite (ACL):
- Jiwei Li and Sujian Li. 2013. A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization. Transactions of the Association for Computational Linguistics, 1:89–98.
- Cite (Informal):
- A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization (Li & Li, TACL 2013)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/Q13-1008.pdf