Automatic data abstraction is an important capability for both benchmarking
machine intelligence and supporting summarization applications. In the former
one asks whether a machine can `understand' enough about the meaning of input
data to produce a meaningful but more compact abstraction. In the latter this
capability is exploited for saving space or human time by summarizing the
essence of input data. In this paper we study a general reinforcement learning
based framework for learning to abstract sequential data in a goal-driven way.
The ability to define different abstraction goals uniquely allows different
aspects of the input data to be preserved according to the ultimate purpose of
the abstraction. Our reinforcement learning objective does not require
human-defined examples of ideal abstraction. Importantly our model processes
the input sequence holistically without being constrained by the original input
order. Our framework is also domain agnostic -- we demonstrate applications to
sketch, video and text data and achieve promising results in all domains.

采用一种基于强化学习实现的框架，实现了自动对序列数据进行目标驱动的数据抽象。该框架在不受数据输入顺序限制的同时，还可以定义不同的目标，以便以最好的方式保留输入数据的不同方面，能够在涉及手稿、视频和文本数据的情况下实现最佳抽象结果。