APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
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.
Cite
@article{arxiv.1808.09658,
title = {APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning},
author = {Yang Gao and Christian M. Meyer and Iryna Gurevych},
journal= {arXiv preprint arXiv:1808.09658},
year = {2018}
}
Comments
EMNLP 2018