@inproceedings{p-v-s-meyer-2017-joint,
title = "Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback",
author = "P.V.S, Avinesh and
Meyer, Christian M.",
editor = "Barzilay, Regina and
Kan, Min-Yen",
booktitle = "Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2017",
address = "Vancouver, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/P17-1124/",
doi = "10.18653/v1/P17-1124",
pages = "1353--1363",
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."
}
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%0 Conference Proceedings
%T Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback
%A P.V.S, Avinesh
%A Meyer, Christian M.
%Y Barzilay, Regina
%Y Kan, Min-Yen
%S Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2017
%8 July
%I Association for Computational Linguistics
%C Vancouver, Canada
%F p-v-s-meyer-2017-joint
%X 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.
%R 10.18653/v1/P17-1124
%U https://aclanthology.org/P17-1124/
%U https://doi.org/10.18653/v1/P17-1124
%P 1353-1363
Markdown (Informal)
[Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback](https://aclanthology.org/P17-1124/) (P.V.S & Meyer, ACL 2017)
ACL