%0 Conference Paper %1 nguyen2019efficient %A Nguyen, Thi-Huyen %A Hoang, Tuan-Anh %A Nejdl, Wolfgang %B In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM) %D 2019 %I SIAM %K breakingnews myown summarization twitterstream %P 226-234 %T Efficient Summarizing of Evolving Events from Twitter Streams %U https://epubs.siam.org/doi/10.1137/1.9781611975673.26 %X Twitter has been heavily used for users to report and share information about real-world events. However, understanding the multiple aspects of an event as it happens is a very challenging task due to the prevalent noise and redundant in tweets as well as the evolution of the event. In this paper, we present a graph-based method for summarizing evolutionary events from tweet streams. Unlike existing approaches that either require prior information, result in less readable summaries, or are not scalable, our proposed method can automatically extract sets of representative tweets as concise summaries for the events. Moreover, the method also allows the summaries to be updated efficiently using an incremental procedure, thus can scale up to large data streams. The experiments on five datasets reveal that our proposed method significantly outperforms several baselines. %@ 978-1-61197-567-3

@inproceedings{nguyen2019efficient, abstract = {Twitter has been heavily used for users to report and share information about real-world events. However, understanding the multiple aspects of an event as it happens is a very challenging task due to the prevalent noise and redundant in tweets as well as the evolution of the event. In this paper, we present a graph-based method for summarizing evolutionary events from tweet streams. Unlike existing approaches that either require prior information, result in less readable summaries, or are not scalable, our proposed method can automatically extract sets of representative tweets as concise summaries for the events. Moreover, the method also allows the summaries to be updated efficiently using an incremental procedure, thus can scale up to large data streams. The experiments on five datasets reveal that our proposed method significantly outperforms several baselines.}, added-at = {2020-01-21T12:35:03.000+0100}, author = {Nguyen, Thi-Huyen and Hoang, Tuan-Anh and Nejdl, Wolfgang}, biburl = {https://www.bibsonomy.org/bibtex/218c49738bd3115507c3ad57c0afeedee/huyen}, booktitle = {In Proceedings of the 2019 SIAM International Conference on Data Mining (SDM)}, crossref = {conf/sdm/2019}, ee = {https://doi.org/10.1137/1.9781611975673.26}, eventdate = {May 2-4, 2019}, interhash = {b2aff8bcc444d38bd6f571afab752ecc}, intrahash = {18c49738bd3115507c3ad57c0afeedee}, isbn = {978-1-61197-567-3}, keywords = {breakingnews myown summarization twitterstream}, pages = {226-234}, publisher = {SIAM}, timestamp = {2024-02-06T06:52:11.000+0100}, title = {Efficient Summarizing of Evolving Events from Twitter Streams}, url = {https://epubs.siam.org/doi/10.1137/1.9781611975673.26}, venue = {Calgary, Alberta, Canada}, year = 2019 }