The document presents a new approach called TSCAN for temporally summarizing topics from a collection of documents. TSCAN first derives the major themes of a topic from the eigenvectors of a temporal block association matrix. It then extracts significant events and their summaries for each theme by examining the eigenvectors. Finally, it associates the extracted events based on their temporal closeness and context similarity to form an evolution graph of the topic. Experiments on the TDT4 corpus show that temporal summaries generated by TSCAN present topics in a comprehensible form and are superior to existing summarization methods based on human references.

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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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