  Procedural text understanding requires machines to reason about entity states
within the dynamical narratives. Current procedural text understanding
approaches are commonly \textbf{entity-wise}, which separately track each
entity and independently predict different states of each entity. Such an
entity-wise paradigm does not consider the interaction between entities and
their states. In this paper, we propose a new \textbf{scene-wise} paradigm for
procedural text understanding, which jointly tracks states of all entities in a
scene-by-scene manner. Based on this paradigm, we propose \textbf{S}cene
\textbf{G}raph \textbf{R}easoner (\textbf{SGR}), which introduces a series of
dynamically evolving scene graphs to jointly formulate the evolution of
entities, states and their associations throughout the narrative. In this way,
the deep interactions between all entities and states can be jointly captured
and simultaneously derived from scene graphs. Experiments show that SGR not
only achieves the new state-of-the-art performance but also significantly
accelerates the speed of reasoning.
