Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization
| Type of publication: | Inproceedings |
| Citation: | ZopfLozaFuernkranz2016IUS |
| Booktitle: | Proceedings of the 26th International Conference on Computational Linguistics |
| Year: | 2016 |
| Month: | December |
| Pages: | 1071-1082 |
| Publisher: | The COLING 2016 Organizing Committee |
| Location: | Osaka, Japan |
| URL: | http://aclweb.org/anthology/C16-1102 |
| Abstract: | 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. |
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