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| | MLIM: Chapter 3 |
 | | At a high level, their goal is the same: find those portion(s) of the given text(s) that are relevant to the users task, and deliver that information to the user in the form most useful for further (human or machine) processing. |
 | | In contrast, Text Summarization does not necessarily start with a predefined set of criteria of interest; when it does, they are not specified as a template, but at a higher granularity (i.e., expressed in keywords or even whole paragraphs), and hence are less computationally precise. |
 | | Given a newspaper text, its task was to recognize which of approximately seven event templates (earthquake, visit of state, terrorism event, etc.) to employ, and then to fill in the templates slots with relevant information. |
| www.cs.cmu.edu /~ref/mlim/chapter3.html (6227 words) |
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