  The ability to use inductive reasoning to extract general rules from multiple
observations is a vital indicator of intelligence. As humans, we use this
ability to not only interpret the world around us, but also to predict the
outcomes of the various interactions we experience. Generalising over multiple
observations is a task that has historically presented difficulties for
machines to grasp, especially when requiring computer vision. In this paper, we
propose a model that can extract general rules from video demonstrations by
simultaneously performing summarisation and translation. Our approach differs
from prior works by framing the problem as a multi-sequence-to-sequence task,
wherein summarisation is learnt by the model. This allows our model to utilise
edge cases that would otherwise be suppressed or discarded by traditional
summarisation techniques. Additionally, we show that our approach can handle
noisy specifications without the need for additional filtering methods. We
evaluate our model by synthesising programs from video demonstrations in the
Vizdoom environment achieving state-of-the-art results with a relative increase
of 11.75% program accuracy on prior works
