A Novel Feature-based Bayesian Model for Query Focused Multi-document Summarization

Jiwei Li, Sujian Li


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
Bibkey:
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)
Copy Citation:
PDF:
https://aclanthology.org/Q13-1008.pdf