P. V. S., Avinesh ; Meyer, Christian M. (2017)
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback.
Vancouver, Canada
doi: 10.18653/v1/P17-1124
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | P. V. S., Avinesh ; Meyer, Christian M. |
| Art des Eintrags: | Bibliographie |
| Titel: | Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2017 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (ACL 2017) |
| Band einer Reihe: | Volume 1: Long Paper |
| Veranstaltungsort: | Vancouver, Canada |
| DOI: | 10.18653/v1/P17-1124 |
| URL / URN: | http://www.aclweb.org/anthology/P17-1124 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | UKP_a_WALL;UKP_reviewed;AIPHES_corpus;AIPHES_area_d2 |
| ID-Nummer: | TUD-CS-2017-0077 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung DFG-Graduiertenkollegs DFG-Graduiertenkollegs > Graduiertenkolleg 1994 Adaptive Informationsaufbereitung aus heterogenen Quellen |
| Hinterlegungsdatum: | 03 Apr 2017 07:44 |
| Letzte Änderung: | 19 Aug 2021 10:53 |
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