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ROUGE is a collection of tests that enable fast analysis of the accuracy of a generated summary.
In this case, external text can be inserted into the generated summary.
In phase 1 problem engagement, we can see each activity has an automatically generated summary according to the actors, resources, tools, and artifacts involved with it.
where n match and n no-match are, respectively, the number of matching and non-matching key-frames between the computed and the user generated summary and n US is the total number of key-frames in the summary.
We generated summary statistics using means or proportions, as appropriate.
We generated summary statistics of sample characteristics and outcomes for both cohorts.
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The tests compare snippets of generated summaries with snippets from accepted summaries.
The experimental results demonstrate that our proposed summarization approach is able to generate summaries effectively, and those generated summaries are perceived as helpful to support mobile learning.
ROUGE-L measure uses the longest common subsequence (LCS), ROUGE-W measure is derived using weighted LCS and ROUGE-SU measure uses skip-bigram plus unigram for measuring the generated summaries [56].
We generated summaries of survey responses related to two factors: heat preparedness and response.
From the data they entered we generated summaries and reports specifically for patients with acute respiratory infections.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com