Contrastive Reasons Detection and Clustering from Online Polarized Debate
This work addresses the challenge of automatically identifying and clustering divergent viewpoints in online debates, which is incremental as it builds on existing contrastive summarization methods.
The paper tackled the problem of unsupervised modeling and extraction of contrastive reasons from polarized online debates, proposing a pipeline approach that significantly improved over state-of-the-art methods in contrastive summarization on such datasets.
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.