  Understanding the steps required to perform a task is an important skill for
AI systems. Learning these steps from instructional videos involves two
subproblems: (i) identifying the temporal boundary of sequentially occurring
segments and (ii) summarizing these steps in natural language. We refer to this
task as Procedure Segmentation and Summarization (PSS). In this paper, we take
a closer look at PSS and propose three fundamental improvements over current
methods. The segmentation task is critical, as generating a correct summary
requires each step of the procedure to be correctly identified. However,
current segmentation metrics often overestimate the segmentation quality
because they do not consider the temporal order of segments. In our first
contribution, we propose a new segmentation metric that takes into account the
order of segments, giving a more reliable measure of the accuracy of a given
predicted segmentation. Current PSS methods are typically trained by proposing
segments, matching them with the ground truth and computing a loss. However,
much like segmentation metrics, existing matching algorithms do not consider
the temporal order of the mapping between candidate segments and the ground
truth. In our second contribution, we propose a matching algorithm that
constrains the temporal order of segment mapping, and is also differentiable.
Lastly, we introduce multi-modal feature training for PSS, which further
improves segmentation. We evaluate our approach on two instructional video
datasets (YouCook2 and Tasty) and observe an improvement over the
state-of-the-art of $\sim7\%$ and $\sim2.5\%$ for procedure segmentation and
summarization, respectively.
