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.- Anthology ID:
- P17-1124
- Volume:
- Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2017
- Address:
- Vancouver, Canada
- Editors:
- Regina Barzilay, Min-Yen Kan
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1353–1363
- Language:
- URL:
- https://aclanthology.org/P17-1124
- DOI:
- 10.18653/v1/P17-1124
- Cite (ACL):
- Avinesh P.V.S and Christian M. Meyer. 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 (Volume 1: Long Papers), pages 1353–1363, Vancouver, Canada. Association for Computational Linguistics.
- Cite (Informal):
- Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback (P.V.S & Meyer, ACL 2017)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/P17-1124.pdf