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

P. V. S., Avinesh and Meyer, Christian M. (2017):
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback.
In: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017), Association for Computational Linguistics, Vancouver, Canada, DOI: 10.18653/v1/P17-1124, [Online-Edition: http://www.aclweb.org/anthology/P17-1124],
[Conference or Workshop Item]

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.

Item Type: Conference or Workshop Item
Erschienen: 2017
Creators: P. V. S., Avinesh and Meyer, Christian M.
Title: Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
Language: English
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.

Title of Book: Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017)
Volume: Volume 1: Long Paper
Publisher: Association for Computational Linguistics
Uncontrolled Keywords: UKP_a_WALL;UKP_reviewed;AIPHES_corpus;AIPHES_area_d2
Divisions: 20 Department of Computer Science
20 Department of Computer Science > Ubiquitous Knowledge Processing
DFG-Graduiertenkollegs
DFG-Graduiertenkollegs > Research Training Group 1994 Adaptive Preparation of Information from Heterogeneous Sources
Event Location: Vancouver, Canada
Date Deposited: 03 Apr 2017 07:44
DOI: 10.18653/v1/P17-1124
Official URL: http://www.aclweb.org/anthology/P17-1124
Identification Number: TUD-CS-2017-0077
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