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In order to evaluate the performance of a new dynamic text summarization extension (Combo) within Semantic MEDLINE, we applied it, plus conventional Semantic MEDLINE, and a baseline summarization methodology (designed to mimic manual clinical review) to a clinical decision support task.
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We then processed the SemRep output using the three summarization methodologies.
Reference standards including recommendations from all three products may be more comprehensive, and shed better light on all three summarization methodologies' recall and precision performance.
The systematic summarization of methodology is as follows: (Fig. 2).
Combo summarization outperformed the baseline methodology by an average F1-score margin of 0.21.
Fig. 1 Methodology of multi document summarization Fig. 2 Stages in MapReduce framework for multi document summarization.
Dynamic and conventional summarization were superior to the baseline methodology.
In Section 2, we discuss our methodology on data collection, summarization, representation, and analysis.
The Combo system was also compared to a baseline as well as a conventional summarization method within the Semantic MEDLINE methodology.
Fuzzy Set Qualitative Comparative Analysis (fsQCA) is a methodology for obtaining linguistic summarizations from data that are associated with cases.
Dynamic summarization with the Combo algorithm outperformed the baseline methodology, which produced an average recall of 0.27 and an average precision of 0.25.
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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