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We created summary categories based on the original survey questions.
Moderators made detailed notes during each session and created summary reports based on their observations.
We used RevMan 2014 for data analysis and storage, and created 'Summary of findings' tables with GRADEpro 2014 software.
Finally, we created summary descriptions of each unit using descriptions of each theme and quotes pertaining to the unit.
Thus, we created summary variables that accounted for time variability and reflected the cross-sectional nature of the analysis.
We extracted data on clinical setting, study design, group size, content source, time and personnel involved; and created summary tables.
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With this method, the researchers created summaries with greater information.
To evaluate the quality of the created summaries, a user evaluation methodology was designed, and applied with promising results, showing that the developed application is able to summarize with success generic audiovisual content, especially high action content.
We created summaries so that each contained 1 2 sentences from each CoreSC category (Hypothesis, Background, etc)., extracted from the original paper, following the distribution of categories in the paper.
Additionally, atomate creates output databases that organize the results from individual calculations and contains a builder framework that creates summary reports for each computed material based on multiple simulations.
Considering semantic similar terms provide benefits in terms of generating more relevant summary but it is more compute intensive, since semantic terms will be generated and considered for creating summary from a large text collection.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com