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.
Highlights
The task of producing summaries from a cluster of multiple topic-related documents has gained much attention during the Document Understanding Conference1 (DUC) and the Text Analysis Conference2 (TAC) series
An even bigger challenge is the high degree of subjectivity in content selection, as it can be seen in the small overlap of what is considered important by different users
We propose a novel integer linear programming (ILP)-based approach using interactive user feedback to create multi-document user-desired summaries
Summary
The task of producing summaries from a cluster of multiple topic-related documents has gained much attention during the Document Understanding Conference (DUC) and the Text Analysis Conference (TAC) series. An even bigger challenge is the high degree of subjectivity in content selection, as it can be seen in the small overlap of what is considered important by different users. Optimizing a system towards one single best summary that fits all users, as it is assumed by current state-of-the-art systems, is highly impractical and diminishes the usefulness of a system for real-world use cases. We propose an interactive conceptbased model to assist users in creating a personalized summary based on their feedback. There have been previous attempts to assist users in single-document summarization, no existing work tackles the problem of multi-document summaries using optimization techniques for user feedback. We put the human in the loop and create a personalized summary that better captures the users’ needs and their different notions of importance
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
