Unveiling Global Discourse Structures: Theoretical Analysis and NLP Applications in Argument Mining

TL;DR

This paper reviews and advances methods for analyzing and extracting global discourse structures in persuasive texts, aiming to improve argument mining through novel NLP techniques and theoretical insights.

Contribution

It introduces a new architecture for argument mining that enhances model generalizability and addresses current research shortcomings.

Findings

Summarizes existing research on discourse structure analysis.

Proposes an NLP pipeline for argument component extraction.

Identifies challenges and outlines solutions for better argument mining.

Abstract

Particularly in the structure of global discourse, coherence plays a pivotal role in human text comprehension and is a hallmark of high-quality text. This is especially true for persuasive texts, where coherent argument structures support claims effectively. This paper discusses and proposes methods for detecting, extracting and representing these global discourse structures in a proccess called Argument(ation) Mining. We begin by defining key terms and processes of discourse structure analysis, then continue to summarize existing research on the matter, and identify shortcomings in current argument component extraction and classification methods. Furthermore, we will outline an architecture for argument mining that focuses on making models more generalisable while overcoming challenges in the current field of research by utilizing novel NLP techniques. This paper reviews current knowledge, summarizes recent works, and outlines our NLP pipeline, aiming to contribute to the theoretical understanding of global discourse structures.