Text summarization attempts to address the information overload
problem by taking a partially-structured source text, extracting
information content from it, and presenting the most important content
to the user in a manner sensitive to the user's or application's
needs. The first part of the talk will describe WebSumm, a system for
summarizing related documents. The approach in WebSumm exploits recent
progress in information extraction to represent salient units of text
and their relationships. By exploiting meaningful relations between
units based on an analysis of text cohesion and the context in which
the comparison is desired, the summarizer can pinpoint similarities
and differences, and align text segments. The second part of the talk
will describe an application of machine learning methods to train our
summarizer. The goal of this learning approach is to have a system
capable of adjusting summarizers to better fit the user's interest.
Date: Thurs., March 26; Time: 4:15-5:30PM; Place: Gates 100
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