Dr. Christian M. Meyer

Joint Optimization
of User-desired Content

in Multi-document Summaries
by Learning from User Feedback

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.

Submitted: 07.02.2017 | Published: 30.07.2017
Comparison of different interactive models.
Comparison of different interactive models.