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.

通过介绍多个方面的多样性，包括意见、注释者和来源，评估了目前在关键点分析任务中表现欠佳的高层次论证总结方法，并发现通用 LLM 和专用 KPA 模型在处理数据来源、代表少数人的论点和人类提供的主观性方面存在互补的优势，观察到多样化的训练数据可能改善泛化能力，因此解决论证总结中的多样性问题需要采取多种策略以应对主观性。