Contrastive Reasons Detection and Clustering from Online Polarized Debate
University of Alberta（阿尔伯塔大学）
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
