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Freedom of choice: |
| An increasingly important aspect for Information Technology
solutions is the ability for those solutions to be consumed by anyone, any where
and on
any platform. Extractor is a patented content summarization technology
researched and developed to work on any computing platform. From its
base in ANSI C the commercial Extractor Software Development Kit is
ready to be consumed on: |
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Linux,
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Solaris and
¤ Windows |
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| Other platforms are
available upon custom request. Or, purchase the
source code and compile to your own precise
specifications. |
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| In true cross-platform consistency, the Extractor Software Development
Kit (SDK) includes supporting API's for these development
languages: |
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¤ C (C, C++, VC++)
¤ Java
¤ Visual Basic
¤ Python
¤ Perl |
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| In addition to the cross platform flexibility, Extractor's internal
features are fully exposed to the developer for customizable
implementations: |
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Generate summaries automatically |
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Native file formats
support: Text, HTML, and Email
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HTML Tag filtering |
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Text, HTML and E-mail
filters |
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Document highlighting and
Sentence marking |
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Multi-lingual*:
English, French, German, Japanese,
Korean & Spanish
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Multi-Threaded |
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Define summary results - set
the number of desired
output
phrases |
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Stop Word - list any
number of words for Extractor to ignore |
¤ Go Word, Go Phrase - list any
number of words/
phrases for
Extractor to focus on |
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Frequency Ranking -
rank summary results in ascending or
descending
order, with or without percentage values |
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Multi-document processing
- summarize multiple documents
simultaneously |
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| In terms of computer
automated text summarization there are many definitions and
implementations including Bayesian, Heurstic or linguistic. Extractor
uses a Genetic approach which in itself provides an automatic learning process.
This is a critical element for the summarization utility to be able
to move from one
subject domain to another without re-training, as well, human
intervention. Compared to other approaches which are
domain specific and anchored by their static
algorithm, thereby requiring greater human
intervention just to be able to move from one subject domain to another. For a
more detailed discussion please see "Learning
Algorithms for Keyphrase Extraction" |
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