  Presenting high-level arguments is a crucial task for fostering participation
in online societal discussions. Current argument summarization approaches miss
an important facet of this task -- capturing diversity -- which is important
for accommodating multiple perspectives. We introduce three aspects of
diversity: those of opinions, annotators, and sources. We evaluate approaches
to a popular argument summarization task called Key Point Analysis, which shows
how these approaches struggle to (1) represent arguments shared by few people,
(2) deal with data from various sources, and (3) align with subjectivity in
human-provided annotations. We find that both general-purpose LLMs and
dedicated KPA models exhibit this behavior, but have complementary strengths.
Further, we observe that diversification of training data may ameliorate
generalization. Addressing diversity in argument summarization requires a mix
of strategies to deal with subjectivity.
