Difference between revisions of "Temporal Summarization of Event-Related Updates in Wikipedia"
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| − | '''Temporal Summarization of Event-Related Updates in Wikipedia''' - scientific work related to Wikipedia quality published in 2013, written by Mihai Georgescu, Dang Duc Pham, Nattiya Kanhabua, Sergej Zerr, Stefan Siersdorfer and Wolfgang Nejdl. | + | '''Temporal Summarization of Event-Related Updates in Wikipedia''' - scientific work related to [[Wikipedia quality]] published in 2013, written by [[Mihai Georgescu]], [[Dang Duc Pham]], [[Nattiya Kanhabua]], [[Sergej Zerr]], [[Stefan Siersdorfer]] and [[Wolfgang Nejdl]]. |
== Overview == | == Overview == | ||
| − | Wikipedia is a free multilingual online encyclopedia covering a wide range of general and specific knowledge. Its content is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events influence the collaborative editing of Wikipedia articles of the involved or affected entities. In this paper, authors present Wikipedia Event Reporter , a web-based system that supports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detection and automatically extracts event-related updates using a machine-learning approach. Further, the system determines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful temporal summarization of event-related updates and automatically annotates the identified events in a timeline. | + | Wikipedia is a free [[multilingual]] online encyclopedia covering a wide range of general and specific knowledge. Its content is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events influence the collaborative editing of [[Wikipedia]] articles of the involved or affected entities. In this paper, authors present Wikipedia Event Reporter , a web-based system that supports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detection and automatically extracts event-related updates using a machine-learning approach. Further, the system determines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful temporal summarization of event-related updates and automatically annotates the identified events in a timeline. |
Revision as of 22:57, 4 July 2019
Temporal Summarization of Event-Related Updates in Wikipedia - scientific work related to Wikipedia quality published in 2013, written by Mihai Georgescu, Dang Duc Pham, Nattiya Kanhabua, Sergej Zerr, Stefan Siersdorfer and Wolfgang Nejdl.
Overview
Wikipedia is a free multilingual online encyclopedia covering a wide range of general and specific knowledge. Its content is continuously maintained up-to-date and extended by a supporting community. In many cases, real-world events influence the collaborative editing of Wikipedia articles of the involved or affected entities. In this paper, authors present Wikipedia Event Reporter , a web-based system that supports the entity-centric, temporal analytics of event-related information in Wikipedia by analyzing the whole history of article updates. For a given entity, the system first identifies peaks of update activities for the entity using burst detection and automatically extracts event-related updates using a machine-learning approach. Further, the system determines distinct events through the clustering of updates by exploiting different types of information such as update time, textual similarity, and the position of the updates within an article. Finally, the system generates the meaningful temporal summarization of event-related updates and automatically annotates the identified events in a timeline.
