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TSCAN-A-Content-Anatomy-Approach-to-Temporal-Topic-
Summarization
Abstract
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.
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