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arXiv · 1908.00648

Contrastive Reasons Detection and Clustering from Online Polarized Debate

Amine Trabelsi·Osmar R. Zaiane
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Abstract

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.

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Amine Trabelsi, Osmar R. Zaiane. 2019-08-01. Contrastive Reasons Detection and Clustering from Online Polarized Debate. https://arxiv.org/abs/1908.00648

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