## 1 Introduction  
Currently, most decisions made by higher-ups in organizations are made in groups [1]. Group decision making (GDM) is a process where a group of people, usually called decision makers, select one or more alternatives to solve a specific problem they are discussing. Typically, this is a procedure where the decision makers discuss their viewpoints and opinions to achieve a consensus. There are several advantages associated with group decision-making processes, such as improving the quality of the decision made or sharing the workload. Nevertheless, the right conditions need to be acquired to take advantage of this process, such as the possibility of interaction between decision makers, allowing them to exchange ideas and the ability to understand the reasoning behind different preferences [2, 3, 4]. Group decision support systems (GDSSs) have been widely studied over the recent decades to create these conditions, so that decision makers can decide effectively. Due to globalization, higher-ups have been dispersed throughout the globe with different time zones, which led to the creation of GDSSs that could solve these location and time issues, the web-based GDSS. The purpose of these systems is to support the group decision-making process by creating the necessary conditions for it to be effective and by allowing the process to be carried out from any place and at any time [5]. These systems provide a way to solve problems, one of the problems being multi-criteria problems. Multi-criteria problems consist of a finite number of alternatives, which are known initially before solving the problem [6], and a limited set of criteria that enable comparing alternatives. These criteria should be accurate, coherent with the decision and its domain, feasible, independent of each other, and measurable [7]. When the definition of alternatives and criteria are completed, a multi-criteria decision-making problem is created, and decision makers can express their opinion, arguing on the available alternatives, valuable through the criteria set. In GDSS, argumentation is ideal, since it makes it easier to use justifications and explanations in interactions between decision makers, allowing them to express their ideas clearly. In addition to that, argumentation can be used to influence their preferences and, subsequently, the outcome of the decision-making process, as well as aiding the creation of higher quality agreements and, at the same time, decreasing the number of unsuccessful negotiations [8]. It is essential to notice that these systems based on argumentation dialogues can generate vast amounts of information, since a group decision-making process usually spans several iterations (rounds), making it difficult for the decision makers to analyze and follow the decision-making process. In addition to that, these systems are not widely accepted in organizations due to several factors, such as the resistance to change from organizations and the fear of losing the value obtained from physical meetings, but mainly due to the lack of explanations on how the system is proposing such solutions [9; 10]. To make GDSSs more appealing to organizations, machine learning is beginning to gradually be used in GDSS to enhance their capabilities, for example, through argument mining (AM). AM consists of the automatic identification and extraction of the structure of inference and reasoning expressed as arguments presented in the natural language [11]. AM is helping GDSS to become more attractive to organizations by automatically obtaining meaningful information from unstructured text, such as aspect terms, aspect categories, and polarity detection field [12]. AM can automatically extract data from the discussion's unstructured text natural language and then present those data to decision makers or, for instance, highlight the relevant messages. These improvements decrease the time participants must take to set up their preferences in a GDSS.