  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.
