  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.
