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In this regard, we extract two summarization versions of a movie as the following strategies.
Non-cognitive methods extract video/movie summarization versions using user feedback [4].
To extract summarization versions, we assume that the appearances of protagonist and characters who have the high value of centralities are the most important factors.
The experimental evaluation results show that this study should index and extract summarization versions while keeping the understanding of the audiences.
We selected a total of 17 episodes from the Star Wars series MV: 1, 2, 3, 4, 5, 6, The Lord of Rings series MV: 7, 8, 9 and the Harry Potter series MV: 10, 11, 12, 13, 14, 15, 16, 17 to index and extract the summarization versions.
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Movie analysis research takes into account extracting summarization version where the goal is to summarize a movie to provide an effective shortened version of the film in a condensed and succinct representation of the storyline.
Besides, we use the user survey question to measure the understanding of user for each summarization version.
Results of extracting summarization version of the given movie are illustrated in Table 2 and Fig. 7.
The appearances and co-appearances of the characters have become the most important roles to extract the summarization version of the given movie.
Results of evaluation experimental showed that our study should extract the summarization version while the users still understand the given movie.
Algorithm 1 illustrates the algorithm for extracting a version (version 1) of summarization by selecting the appearance of the protagonist of the given movie.
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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