Online discussion platforms are a vital part of the public discourse in a deliberative democracy. However, how to interpret the outcomes of the discussions on these platforms is often unclear. In this paper, we propose a novel and explainable method for selecting a set of most representative, consistent points of view by combining methods from computational social choice and abstract argumentation. Specifically, we model online discussions as abstract argumentation frameworks combined with information regarding which arguments voters approve of. Based on ideas from approval-based multiwinner voting, we introduce several voting rules for selecting a set of preferred extensions that represents voters' points of view. We compare the proposed methods across several dimensions, theoretically and in numerical simulations, and give clear suggestions on which methods to use depending on the specific situation.

翻译：在线讨论平台是协商民主中公共话语的重要组成部分。然而，如何解读这些平台上讨论的结果往往并不明确。在本文中，我们提出了一种新颖且可解释的方法，通过结合计算社会选择与抽象论证的方法，来选取一组最具代表性且一致的观点。具体而言，我们将在线讨论建模为抽象论证框架，并结合了关于哪些论点获得投票者认可的信息。基于批准式多赢家投票的思想，我们引入了几种投票规则，用于选取一组能够代表投票者观点的偏好扩展。我们从多个维度对这些方法进行了理论和数值模拟的比较，并针对不同具体情况给出了明确的方法选用建议。