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.

翻译：提出高层次论点是促进在线社会讨论参与的关键任务。当前的论元摘要方法忽略了这一任务的重要维度——捕捉多样性——这对容纳多元视角至关重要。我们从三个维度定义多样性：观点多样性、标注者多样性和来源多样性。通过评估主流论元摘要任务"关键点分析"的各种方法，我们发现这些方法难以：(1) 呈现少数群体共享的论点，(2) 处理多源异构数据，以及(3) 契合人工标注中的主观性。研究表明，通用大语言模型与专用KPA模型均存在此特征，但两者具有互补优势。进一步观察到，训练数据的多样化可能提升泛化能力。解决论元摘要中的多样性问题需要综合运用多种策略来应对主观性。