APRIL : Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning. / Gao, Yang; Meyer, Christian M.; Gurevych, Iryna.

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium : Association for Computational Linguistics, 2018. p. 4120-4130.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Published
  • Yang Gao
  • Christian M. Meyer
  • Iryna Gurevych

Abstract

We propose a method to perform automatic document summarisation without using reference summaries. Instead, our method interactively learns from users’ preferences. The merit of preference-based interactive summarisation is that preferences are easier for users to provide than reference summaries. Existing preference-based interactive learning methods suffer from high sample complexity, i.e. they need to interact with the oracle for many rounds in order to converge. In this work, we propose a new objective function, which enables us to leverage active learning, preference learning and reinforcement learning techniques in order to reduce the sample complexity. Both simulation and real-user experiments suggest that our method significantly advances the state of the art. Our source code is freely available at https://github.com/UKPLab/emnlp2018-april.
Original languageEnglish
Title of host publicationProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Place of PublicationBrussels, Belgium
PublisherAssociation for Computational Linguistics
Pages4120-4130
Number of pages11
DOIs
Publication statusPublished - 31 Oct 2018
This open access research output is licenced under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License.

ID: 34292201