TY - JOUR
T1 - Remembering winter was coming
T2 - Character-oriented video summaries of TV series
AU - Bost, Xavier
AU - Gueye, Serigne
AU - Labatut, Vincent
AU - Larson, Martha
AU - Linarès, Georges
AU - Malinas, Damien
AU - Roth, Raphaël
PY - 2019
Y1 - 2019
N2 - Today’s popular tv series tend to develop continuous, complex plots spanning several seasons, but are often viewed in controlled and discontinuous conditions. Consequently, most viewers need to be re-immersed in the story before watching a new season. Although discussions with friends and family can help, we observe that most viewers make extensive use of summaries to re-engage with the plot. Automatic generation of video summaries of tv series’ complex stories requires, first, modeling the dynamics of the plot and, second, extracting relevant sequences. In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character’s social environment suggest a new development relevant for the summary. Once identified, these major stages in each character’s storyline can be used as a basis for completing the summary with related sequences. Our algorithm combines such social network analysis with filmmaking grammar to automatically generate character-oriented video summaries of tv series from partially annotated data. We carry out evaluation with a user study in a real-world scenario: a large sample of viewers were asked to rank video summaries centered on five characters of the popular tv series Game of Thrones, a few weeks before the new, sixth season was released. Our results reveal the ability of character-oriented summaries to re-engage viewers in television series and confirm the contributions of modeling the plot content and exploiting stylistic patterns to identify salient sequences.
AB - Today’s popular tv series tend to develop continuous, complex plots spanning several seasons, but are often viewed in controlled and discontinuous conditions. Consequently, most viewers need to be re-immersed in the story before watching a new season. Although discussions with friends and family can help, we observe that most viewers make extensive use of summaries to re-engage with the plot. Automatic generation of video summaries of tv series’ complex stories requires, first, modeling the dynamics of the plot and, second, extracting relevant sequences. In this paper, we tackle plot modeling by considering the social network of interactions between the characters involved in the narrative: substantial, durable changes in a major character’s social environment suggest a new development relevant for the summary. Once identified, these major stages in each character’s storyline can be used as a basis for completing the summary with related sequences. Our algorithm combines such social network analysis with filmmaking grammar to automatically generate character-oriented video summaries of tv series from partially annotated data. We carry out evaluation with a user study in a real-world scenario: a large sample of viewers were asked to rank video summaries centered on five characters of the popular tv series Game of Thrones, a few weeks before the new, sixth season was released. Our results reveal the ability of character-oriented summaries to re-engage viewers in television series and confirm the contributions of modeling the plot content and exploiting stylistic patterns to identify salient sequences.
KW - Dynamic social network
KW - Extractive summarization
KW - Plot analysis
KW - tv series
UR - http://www.scopus.com/inward/record.url?scp=85072161380&partnerID=8YFLogxK
U2 - 10.1007/s11042-019-07969-4
DO - 10.1007/s11042-019-07969-4
M3 - Article
AN - SCOPUS:85072161380
SN - 1380-7501
VL - 78
SP - 35373
EP - 35399
JO - Multimedia Tools and Applications
JF - Multimedia Tools and Applications
IS - 24
ER -