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 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.

Adapting Neural Single-Document Summarization Model for Abstractive Multi-Document Summarization: A Pilot Study

“…The study of MDS is pioneered by (McKeown and Radev, 1995), and early notable works also include (McKeown et al, 1999;Radev et al, 2000). Extractive summarization systems that compose a summary from a number of important sentences from the source documents are by far the most popular solution for MDS (Avinesh and Meyer, 2017). Redundancy is one of the biggest problems for extractive methods (Gambhir and Gupta, 2017), and the Maximal Marginal Relevance (MRR) (Carbonell and Goldstein, 1998) is a well-known algorithm for reducing redundancy.…”

“…One important architecture is to model MDS as a budgeted maximum coverage problem, including the prior approach (Mc-Donald, 2007) and improved models (Woodsend and Lapata, 2012;Li et al, 2013;Boudin et al, 2015). There are still recent studies under traditional extractive framework (Peyrard and Eckle-Kohler, 2017;Avinesh and Meyer, 2017).…”

Live blog summarization

“…For comparison, we compute two upper bounds UB-1 and UB-2. The upper bound for extractive summarization is retrieved by solving the maximum coverage of ngrams from the reference summary (Takamura and Okumura 2010;Peyrard and Eckle-Kohler 2016;Avinesh and Meyer 2017). Upper bound summary extraction is cast as an ILP problem as described in Eqs.…”