Goal-Driven Sequential Data Abstraction

Abstract

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.

Accepted at ICCV 2019

References in corpus (6)

• Sequence to Sequence Learning with Neural Networks
• SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents
• A Neural Representation of Sketch Drawings
• Neural Extractive Summarization with Side Information
• Sequential Dual Deep Learning with Shape and Texture Features for Sketch Recognition
• Video Summarisation by Classification with Deep Reinforcement Learning