Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
Avinesh Pvs, Christian M. Meyer · 2017
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 highquality summaries with a minimum number of iterations and feedbacks.We conduct multiple simulation-based experiments and analyze the effect of feedbackbased concept selection in the ILP setup in order to maximize the user-desired content in the summary.