Uncovering Argumentative Flow: A Question-Focus Discourse Structuring Framework (2025.emnlp-main)
Copied to clipboard
| Challenge: | Existing structure modeling approaches fail to capture the author’s rhetorical intent and reasoning process. |
| Approach: | They propose a Question-Focus discourse structuring framework that explicitly models the underlying argumentative flow by anchoring each argumentative unit to a guiding question and a set of attentional foci. |
| Outcome: | The proposed framework outperforms baseline models and curated models on an argument reconstruction task in Chinese think-tank articles and claims coverage. |
Similar Papers
Arg-LLaDA: Argument Summarization via Large Language Diffusion Models and Sufficiency-Aware Refinement (2026.acl-long)
Copied to clipboard
| Challenge: | Existing approaches to argument summarization rely on single-pass generation, offering limited support for factual correction or structural refinement. |
| Approach: | They propose a large language diffusion framework that iteratively improves argument summarization by sufficiency-guided remasking and regeneration. |
| Outcome: | Empirical results show that Arg-LLaDA surpasses state-of-the-art baselines in 7 out of 10 evaluation metrics. |
Exploring Discourse Structures for Argument Impact Classification (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies have shown that discourse structures influence the persuasiveness of arguments. |
| Approach: | They propose to fuse sentence-level structural discourse information with contextualized features derived from large-scale language models to investigate how discourse relations influence argument impact. |
| Outcome: | The proposed model improves its backbone RoBERTa around 1.67%, compared with other models, but side effects are brought by other models. |
Plan Dynamically, Express Rhetorically: A Debate-Driven Rhetorical Framework for Argumentative Writing (2025.emnlp-main)
Copied to clipboard
| Challenge: | Argumentative essay generation (AEG) is a complex task that requires advanced semantic understanding, logical reasoning, and organized integration of perspectives. |
| Approach: | They propose a debate-driven rhetorical framework for argumentative writing that integrates Bitzer’s rhetorical situation theory to improve logical depth, argumentative diversity, and rhetorical persuasiveness. |
| Outcome: | The proposed framework improves logical depth, argumentative diversity, and rhetorical persuasiveness over existing state-of-the-art models. |
A Multi-layer Annotated Corpus of Argumentative Text: From Argument Schemes to Discourse Relations (L18-1)
Copied to clipboard
| Challenge: | Recent interest in Argumentation Mining has brought to the fore the need for corpora annotated with argument information, which can be used as training data. |
| Approach: | They propose a set of guidelines for the annotation of argument schemes and a new annotation tool for the 'inferential' argument schemes. |
| Outcome: | The proposed corpus includes 112 argumentative microtexts and a new annotation tool. |
Towards Comprehensive Argument Analysis in Education: Dataset, Tasks, and Method (2025.acl-long)
Copied to clipboard
| Challenge: | Existing research on argument mining has proposed various argument annotation schemes and tasks. |
| Approach: | They propose a framework comprising 14 fine-grained relation types to capture the interplay between argument components for a thorough understanding of argument structure. |
| Outcome: | The proposed framework captures the interplay between argument components for a thorough understanding of argument structure. |
Decompositional Argument Mining: A General Purpose Approach for Argument Graph Construction (P19-1)
Copied to clipboard
| Challenge: | Argument mining is the process of identifying argumentative structure contained within a text. |
| Approach: | They propose to decompose propositions into four functional components and identify the patterns linking those components to determine argument structure. |
| Outcome: | The proposed method is generic in that it is not tuned for a specific corpus and achieved an F score of 0.79, 0.77 and 0.64 respectively. |
A Structure-Aware Argument Encoder for Literature Discourse Analysis (2022.coling-1)
Copied to clipboard
Yinzi Li, Wei Chen, Zhongyu Wei, Yujun Huang, Chujun Wang, Siyuan Wang, Qi Zhang, Xuanjing Huang, Libo Wu
| Challenge: | Existing research for argument representation learning treats tokens in sentences equally and ignores the implied structure information of argumentative context. |
| Approach: | They propose to separate tokens into two groups to capture structural information of arguments and to incorporate paragraph-level position information into the model. |
| Outcome: | The proposed model captures structural information of arguments and is able to identify arguments automatically. |
Enhancing Argument Structure Extraction with Efficient Leverage of Contextual Information (2023.findings-emnlp)
Copied to clipboard
| Challenge: | Argument structure extraction (ASE) aims to identify the discourse structure of arguments within documents. |
| Approach: | They propose an Efficient Context-aware ASE model that fully exploits contextual information by augmenting modeling capacity and augmenting training data. |
| Outcome: | The proposed model can extract argumentative discourse structure from documents and reduce reliance on specific words or less informative sentences. |
Segmentation of Complex Question Turns for Argument Mining: A Corpus-based Study in the Financial Domain (2024.lrec-main)
Copied to clipboard
| Challenge: | Earnings Conference Calls (ECCs) are a favoured domain for the study of argumentation in context and the extraction of Argumentative Discourse Units (ADUs). |
| Approach: | Earnings Conference Calls (ECCs) are favoured domain for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
| Outcome: | ECCs are favoured for study of argumentation in context and extraction of Argumentative Discourse Units (ADUs). |
Efficient Argument Structure Extraction with Transfer Learning and Active Learning (2022.findings-acl)
Copied to clipboard
| Challenge: | Identifying and understanding the argumentative discourse structure in text has been a critical task in argument mining. |
| Approach: | They propose a context-aware Transformer-based argument structure prediction model that outperforms models that rely on features or only encode limited contexts. |
| Outcome: | The proposed model outperforms models that rely on features or encode limited contexts on five domains and on peer reviews on five different domains. |