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A topic is usually associated with a specific time, place, and person(s). Generally, topics that involve bipolar or competing viewpoints are attention getting and are thus reported in a large number of documents. Identifying the association between important persons mentioned in numerous topic documents would help readers comprehend topics more easily. In this paper, we propose an unsupervised approach for identifying bipolar person names in a set of topic documents. Specifically, we employ principal component analysis (PCA) to discover bipolar word usage patterns of person names in the documents, and show that the signs of the entries in the principal eigenvector of PCA partition the person names into bipolar groups spontaneously. To reduce the effect of data sparseness, we introduce two techniques, called the weighted correlation coefficient and off-topic block elimination. We also present a timeline system that shows the intensity and activeness development of the identified bipolar person groups. Empirical evaluations demonstrate the efficacy of the proposed approach in identifying bipolar person names in topic documents, while the generated timelines provide comprehensive storylines of topics. [ABSTRACT FROM PUBLISHER]

A topic is defined as a seminal event or activity along with all directly related events and activities. It is represented by a chronological sequence of documents published by different authors on the Internet. In this study, we define a task called topic anatomy, which summarizes and associates the core parts of a topic temporally so that readers can understand the content easily. The proposed topic anatomy model, called TSCAN, derives the major themes of a topic from the eigenvectors of a temporal block association matrix. Then, the significant events of the themes and their summaries are extracted by examining the constitution of the eigenvectors. Finally, the extracted events are associated through their temporal closeness and context similarity to form an evolution graph of the topic. Experiments based on the official TDT4 corpus demonstrate that the generated temporal summaries present the storylines of topics in a comprehensible form. Moreover, in terms of content coverage, coherence, and consistency, the summaries are superior to those derived by existing summarization methods based on human-composed reference summaries. [ABSTRACT FROM AUTHOR]