Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback

Avinesh P.V.S, Christian M. Meyer


Abstract
In this paper, we propose an extractive multi-document summarization (MDS) system using joint optimization and active learning for content selection grounded in user feedback. Our method interactively obtains user feedback to gradually improve the results of a state-of-the-art integer linear programming (ILP) framework for MDS. Our methods complement fully automatic methods in producing high-quality summaries with a minimum number of iterations and feedbacks. We conduct multiple simulation-based experiments and analyze the effect of feedback-based concept selection in the ILP setup in order to maximize the user-desired content in the summary.
Anthology ID:
P17-1124
Volume:
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
July
Year:
2017
Address:
Vancouver, Canada
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
1353–1363
Language:
URL:
https://www.aclweb.org/anthology/P17-1124
DOI:
10.18653/v1/P17-1124
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PDF:
http://aclanthology.lst.uni-saarland.de/P17-1124.pdf
Poster:
 P17-1124.Poster.pdf