CrowdStory: Fine-Grained Event Storyline Generation by Fusion of Multi-Modal Crowdsourced Data. Guo, B., Ouyang, Y., Zhang, C., Zhang, J., Yu, Z., Wu, D., & Wang, Y. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol., 1(3):55:1–55:19, September, 2017. ![CrowdStory: Fine-Grained Event Storyline Generation by Fusion of Multi-Modal Crowdsourced Data [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Event summarization based on crowdsourced microblog data is a promising research area, and several researchers have recently focused on this field. However, these previous works fail to characterize the fine-grained evolution of an event and the rich correlations among posts. The semantic associations among the multi-modal data in posts are also not investigated as a means to enhance the summarization performance. To address these issues, this study presents CrowdStory, which aims to characterize an event as a fine-grained, evolutionary, and correlation-rich storyline. A crowd-powered event model and a generic event storyline generation framework are first proposed, based on which a multi-clue–based approach to fine-grained event summarization is presented. The implicit human intelligence (HI) extracted from visual contents and community interactions is then used to identify inter-clue associations. Finally, a cross-media mining approach to selective visual story presentation is proposed. The experiment results indicate that, compared with the state-of-the-art methods, CrowdStory enables fine-grained event summarization (e.g., dynamic evolution) and correctly identifies up to 60% strong correlations (e.g., causality) of clues. The cross-media approach shows diversity and relevancy in visual data selection.
@article{guo_crowdstory:_2017,
title = {{CrowdStory}: {Fine}-{Grained} {Event} {Storyline} {Generation} by {Fusion} of {Multi}-{Modal} {Crowdsourced} {Data}},
volume = {1},
issn = {2474-9567},
shorttitle = {{CrowdStory}},
url = {http://doi.acm.org/10.1145/3130920},
doi = {10.1145/3130920},
abstract = {Event summarization based on crowdsourced microblog data is a promising research area, and several researchers have recently focused on this field. However, these previous works fail to characterize the fine-grained evolution of an event and the rich correlations among posts. The semantic associations among the multi-modal data in posts are also not investigated as a means to enhance the summarization performance. To address these issues, this study presents CrowdStory, which aims to characterize an event as a fine-grained, evolutionary, and correlation-rich storyline. A crowd-powered event model and a generic event storyline generation framework are first proposed, based on which a multi-clue--based approach to fine-grained event summarization is presented. The implicit human intelligence (HI) extracted from visual contents and community interactions is then used to identify inter-clue associations. Finally, a cross-media mining approach to selective visual story presentation is proposed. The experiment results indicate that, compared with the state-of-the-art methods, CrowdStory enables fine-grained event summarization (e.g., dynamic evolution) and correctly identifies up to 60\% strong correlations (e.g., causality) of clues. The cross-media approach shows diversity and relevancy in visual data selection.},
number = {3},
urldate = {2019-01-18},
journal = {Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.},
author = {Guo, Bin and Ouyang, Yi and Zhang, Cheng and Zhang, Jiafan and Yu, Zhiwen and Wu, Di and Wang, Yu},
month = sep,
year = {2017},
pages = {55:1--55:19},
}
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