ID  - ZopfLozaFuernkranz2016IUS
T1  - Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization
A1  - Zopf, Markus
A1  - Loza Mencía, Eneldo
A1  - Fürnkranz, Johannes
TI  - Proceedings of the 26th International Conference on Computational Linguistics
Y1  - 2016
SP  - 1071
EP  - 1082
PB  - The COLING 2016 Organizing Committee
CY  - Osaka, Japan
UR  - http://aclweb.org/anthology/C16-1102
N2  - Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it is crucial to give users access to important and non-redundant information as early as possible. Previous work uses either a fast but inaccurate pipeline approach or a precise but slow clustering approach. Instead, we propose to use sequential clustering for grouping information so that we are able to publish sentences at each time step. By doing so, we combine the best of both clustering and pipeline approaches and create a fast and precise real-time system. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves better results compared to the state-of-the-art.
ER  -