  This paper presents a comprehensive overview of the Comparative Opinion
Mining from Vietnamese Product Reviews shared task (ComOM), held as part of the
10$^{th}$ International Workshop on Vietnamese Language and Speech Processing
(VLSP 2023). The primary objective of this shared task is to advance the field
of natural language processing by developing techniques that proficiently
extract comparative opinions from Vietnamese product reviews. Participants are
challenged to propose models that adeptly extract a comparative "quintuple"
from a comparative sentence, encompassing Subject, Object, Aspect, Predicate,
and Comparison Type Label. We construct a human-annotated dataset comprising
$120$ documents, encompassing $7427$ non-comparative sentences and $2468$
comparisons within $1798$ sentences. Participating models undergo evaluation
and ranking based on the Exact match macro-averaged quintuple F1 score.
