  This work explores the problem of generating task graphs of real-world
activities. Different from prior formulations, we consider a setting where text
transcripts of instructional videos performing a real-world activity (e.g.,
making coffee) are provided and the goal is to identify the key steps relevant
to the task as well as the dependency relationship between these key steps. We
propose a novel task graph generation approach that combines the reasoning
capabilities of instruction-tuned language models along with clustering and
ranking components to generate accurate task graphs in a completely
unsupervised manner. We show that the proposed approach generates more accurate
task graphs compared to a supervised learning approach on tasks from the ProceL
and CrossTask datasets.
