SeqCluSum: Combining Sequential Clustering and Contextual Importance Measuring to Summarize Developing Events over Time
| Type of publication: | Inproceedings |
| Citation: | Zopf2015SeqCluSum |
| Booktitle: | The Twenty-Fourth Text Retrieval Conference Proceedings |
| Year: | 2015 |
| Month: | November |
| Publisher: | National Institute of Standards and Technology |
| Location: | Gaithersburg, Maryland, USA |
| URL: | http://trec.nist.gov/pubs/trec24/papers/AIPHES-TS.pdf |
| Abstract: | Unexpected events such as accidents, natural disasters and terrorist attacks represent an information situation where it isessential to give users access to important and non-redundant information as fastas possible. In this paper, we introduce SeqCluSum, a temporal summarizationsystem which combines sequential clustering to cluster sentences and a contextual importance measurement to weightthe created clusters and thereby to identify important sentences. We participatedwith this system in the TREC TemporalSummarization track where systems haveto generate extractive summaries for developing events by publishing sentencelength updates extracted from web documents. Results show that our approach isvery well suited for this task by achieving best results. We furthermore point outseveral improvement possibilities to showhow the system can further be enhanced. |
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