Using Rhetorical Structures to Analyze Argumentation in Scientific Communication Texts

This article explores the role of rhetorical structures in the argument mining task on the material of scientific Internet communication texts in Russian.Two approaches are proposed and studied for argumentative relation prediction: the first one constructs segment vector representations using a Graph Neural Network (GNN) based on rhetorical structure, and the second one uses multitask learning that combines argumentation extraction with rhetorical relations prediction tasks.With proposed approaches three models were implemented: two variations of the model using GNN and one model employing the multitask approach.These models were compared with a simple baseline using the Lonformer model on a dataset annotated with both argumentative and rhetorical structures.Argumentative annotating was performed manually by four experts.Existing resources and tools were used to obtain rhetorical markup.The conducted experiments showed that the approaches using additional rhetorical information improve the quality of argumentative relation prediction, particularly for long-distance relations.The best performance, with an F1 score of 72.32%, was achieved by a model incorporating GNN-enhanced statement representations.