APRIL：结合主动偏好学习和强化学习的交互式总结学习
APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning
摘要：
我们提出了一种方法来执行自动文档摘要，而不使用参考摘要。相反，我们的方法交互式地从用户的偏好中学习。基于偏好的交互式摘要的优点在于，偏好比参考摘要更容易为用户提供。现有的基于偏好的交互式学习方法存在样本复杂度高的问题，即它们需要与预言机进行多轮交互才能收敛。在这项工作中，我们提出了一个新的目标函数，它使我们能够利用主动学习，偏好学习和强化学习技术，以减少样本的复杂性。模拟和真实用户实验表明，我们的方法显着推进的最先进的。我们的源代码是免费提供的https://github.com/UKPLab/emnlp2018-april。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
