APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
Yang Gao, Christian M. Meyer, Iryna Gurevych · 2018
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.