# Contrastive Reasons Detection and Clustering from Online Polarized Debate

arXiv: https://arxiv.org/abs/1908.00648 · HTML: https://mlsanity.com/paper/1908.00648/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 1 of 1)

- [A Community-Based Approach for Stance Distribution and Argument Organization](https://mlsanity.com/paper/2604.16852/) (2026)

## Abstract (arXiv)

This work tackles the problem of unsupervised modeling and extraction of the main contrastive sentential reasons conveyed by divergent viewpoints on polarized issues. It proposes a pipeline approach centered around the detection and clustering of phrases, assimilated to argument facets using a novel Phrase Author Interaction Topic-Viewpoint model. The evaluation is based on the informativeness, the relevance and the clustering accuracy of extracted reasons. The pipeline approach shows a significant improvement over state-of-the-art methods in contrastive summarization on online debate datasets.