TY - GEN
T1 - Timeline summarization for event-related discussions on a chinese social media platform
AU - Wang, Han
AU - Koh, Jia Ling
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - In this paper, we proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event without supervised learning. In order to select event-related sentences, we designed a two-stage method to extract representative entity terms in the event-related discussions and filter out most of the sentences semantically un-related to the query event. A rule-based method was applied to extract sentences which describing sub-events. After that, the discussions are assigned to the corresponding sub-events according to the semantic relatedness measure. Finally, according to the occurring time of each sub-event, the timeline summarization is organized. We evaluated the performance of the proposed method on the real-world datasets. The experiment results showed that each processing step perform effectively. Especially, most noise sentences could be filtered by the proposed method. Moreover, the final timeline summarization graded by users is proven to be useful to well understand the discussion trend of a sub-event.
AB - In this paper, we proposed an approach to automatically generate timeline summarization for sub-event discussions related to a query event without supervised learning. In order to select event-related sentences, we designed a two-stage method to extract representative entity terms in the event-related discussions and filter out most of the sentences semantically un-related to the query event. A rule-based method was applied to extract sentences which describing sub-events. After that, the discussions are assigned to the corresponding sub-events according to the semantic relatedness measure. Finally, according to the occurring time of each sub-event, the timeline summarization is organized. We evaluated the performance of the proposed method on the real-world datasets. The experiment results showed that each processing step perform effectively. Especially, most noise sentences could be filtered by the proposed method. Moreover, the final timeline summarization graded by users is proven to be useful to well understand the discussion trend of a sub-event.
KW - Sub-event detection
KW - Text data mining
KW - Timeline summarization
UR - http://www.scopus.com/inward/record.url?scp=85026355324&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85026355324&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-60042-0_64
DO - 10.1007/978-3-319-60042-0_64
M3 - Conference contribution
AN - SCOPUS:85026355324
SN - 9783319600413
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 579
EP - 594
BT - Advances in Artificial Intelligence
A2 - Ali, Moonis
A2 - Benferhat, Salem
A2 - Tabia, Karim
PB - Springer Verlag
T2 - 30th International Conference on Industrial, Engineering, and Other Applications of Applied Intelligent Systems, IEA/AIE 2017
Y2 - 27 June 2017 through 30 June 2017
ER -