Information acquisition using multiple classifications

Given a large collection of documents, we often need to extract various aspects of information that may be integrated to form a coherent overall picture. Especially for subjective documents addressing a single topic, traditional summarization techniques are limited in differentiating and clustering similar information. We apply multiple classifications to handle diverse aspects, including subtopic identification, keyword extraction, argument structure analysis, and opinion classification, in order to provide a summarized overview of the collection, complete with distributional information. From this overall summary, system users can effectively obtain more fine-grained information. Our methods for individual modules significantly outperform the baseline and achieve human-level agreement.