# A Community-Based Approach for Stance Distribution and Argument Organization

arXiv: https://arxiv.org/abs/2604.16852 · HTML: https://mlsanity.com/paper/2604.16852/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 22 of its 62 references that are in the map.
- First posted on arXiv 18 Apr 2026.

## Builds on (most cited first, 22 of 22)

- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment](https://mlsanity.com/paper/2303.16634/) (2023)
- [BERTopic: Neural topic modeling with a class-based TF-IDF procedure](https://mlsanity.com/paper/2203.05794/) (2022)
- [SummEval: Re-evaluating Summarization Evaluation](https://mlsanity.com/paper/2007.12626/) (2020)
- [Evaluating the Factual Consistency of Abstractive Text Summarization](https://mlsanity.com/paper/1910.12840/) (2019)
- [We Can Detect Your Bias: Predicting the Political Ideology of News Articles](https://mlsanity.com/paper/2010.05338/) (2020)
- [Seeing Things from a Different Angle: Discovering Diverse Perspectives about Claims](https://mlsanity.com/paper/1906.03538/) (2019)
- [A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis](https://mlsanity.com/paper/1911.11408/) (2019)
- [From Arguments to Key Points: Towards Automatic Argument Summarization](https://mlsanity.com/paper/2005.01619/) (2020)
- [Classification and Clustering of Arguments with Contextualized Word Embeddings](https://mlsanity.com/paper/1906.09821/) (2019)
- [Quantitative Argument Summarization and Beyond: Cross-Domain Key Point Analysis](https://mlsanity.com/paper/2010.05369/) (2020)
- [Every Bite Is an Experience: Key Point Analysis of Business Reviews](https://mlsanity.com/paper/2106.06758/) (2021)
- [Key Point Analysis via Contrastive Learning and Extractive Argument Summarization](https://mlsanity.com/paper/2109.15086/) (2021)
- [Using Summarization to Discover Argument Facets in Online Ideological Dialog](https://mlsanity.com/paper/1709.00662/) (2017)
- [Explaining Controversy on Social Media via Stance Summarization](https://mlsanity.com/paper/1806.07942/) (2018)
- [An Empirical Analysis of Diversity in Argument Summarization](https://mlsanity.com/paper/2402.01535/) (2024)
- [Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection](https://mlsanity.com/paper/2005.07886/) (2020)
- [From Key Points to Key Point Hierarchy: Structured and Expressive Opinion Summarization](https://mlsanity.com/paper/2306.03853/) (2023)
- [Viewpoint Discovery and Understanding in Social Networks](https://mlsanity.com/paper/1810.11047/) (2018)
- [Eva: Attribute-Aware Network Segmentation](https://mlsanity.com/paper/1910.06599/) (2019)
- [Contrastive Reasons Detection and Clustering from Online Polarized Debate](https://mlsanity.com/paper/1908.00648/) (2019)

## Abstract (arXiv)

The proliferation of online debate platforms and social media has led to an unprecedented volume of argumentative content on controversial topics from multiple perspectives. While this wealth of perspectives offers opportunities for developing critical thinking and breaking filter bubbles (Pariser 2011), the sheer volume and complexity of arguments make it challenging for readers to synthesize and comprehend diverse viewpoints effectively. We present an unsupervised graph-based approach for community-based argument organization that helps users navigate and understand complex argumentative landscapes. Our system analyzes collections of topic-focused articles and constructs a rich interaction graph by capturing multiple relationship types between arguments: topic similarity, semantic coherence, shared keywords, and common entities. We then employ community detection to identify argument communities that reveal homogeneous and heterogeneous viewpoint distributions. The detected communities are simplified through strategic graph operations to present users with digestible, yet comprehensive summaries of key argumentative patterns. Our approach requires no training data and can effectively process hundreds of articles while preserving nuanced relationships between arguments. Experimental results demonstrate our system's ability to identify meaningful argument communities and present them in an interpretable manner, facilitating users' understanding of complex socio-political debates.