APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

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.
Anthology ID:
D18-1445
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
4120–4130
Language:
URL:
https://www.aclweb.org/anthology/D18-1445
DOI:
10.18653/v1/D18-1445
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http://aclanthology.lst.uni-saarland.de/D18-1445.pdf
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