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April 2017 — Our paper on summarization has been accepted to ACL — check out the blog post!
Video summarization has been the subject of many recent research works and various algorithms have been proposed in order to tackle the problem [12, 15 20].
As an integrated assessment, summarization has been used in high-stakes international language tests, such as the new TOEFL (Yu 2009).
The interaction network summarization has been described with independent topical events that are temporally and topically coherent and this interaction network has been summarized by large events [156].
The role of reading and writing in summarization has been explored by Cohen (1994) and Sarig (1993), who examined the processes and strategies students engage in reading and writing, and by Asención Delaney (2008) and Yu (2008), who examined the relationship between reading, writing, and summarizing.
The robust multi-array average (rma) normalization (background-adjustment, quantile normalization and median polish summarization) has been performed using RMAExpress version 1.0 beta 4. In addition, we assessed data integrity by calculating Pearson correlation z-values over the complete dataset of 45,101 probe sets.
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Although many solutions of text summarization have been applied by intelligent tutoring systems for learning support, few of them have been quantitatively investigated for learning achievements of learners, especially in mobile learning context.
In [4], different solutions to video summarization have been described in detail.
The evaluation of a key-frame extraction and video summarization systems has been considered a very difficult problem, as long as user-based systems are concerned.
Although many studies have compared GeneChip summarizations, there has been no general consensus.
Due to the underlying challenges of abstractive summarization, more effort has been directed toward the extractive summarization research as seen in TREC (http://trec.nist.gov/), DUC (http://duc.nist.gov/) and TAC (http://www.nist.gov/tac/) conferences.
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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