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        <title>Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization</title>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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            <namePart type="given">Rashmi</namePart>
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    <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. Incremental update summarization (IUS) aims at summarizing events which develop over time. In this paper, we propose a combination of sequential clustering and contextual importance measures to identify important sentences in a stream of documents in a timely manner. Sequential clustering is used to cluster similar sentences. The created clusters are scored by a contextual importance measure which identifies important information as well as redundant information. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves superior results compared to the best participating systems.</abstract>
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%0 Conference Proceedings
%T Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization
%A Zopf, Markus
%A Loza Mencía, Eneldo
%A Fürnkranz, Johannes
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F zopf-etal-2016-sequential
%X 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. Incremental update summarization (IUS) aims at summarizing events which develop over time. In this paper, we propose a combination of sequential clustering and contextual importance measures to identify important sentences in a stream of documents in a timely manner. Sequential clustering is used to cluster similar sentences. The created clusters are scored by a contextual importance measure which identifies important information as well as redundant information. Experiments on the TREC Temporal Summarization 2015 shared task dataset show that our system achieves superior results compared to the best participating systems.
%U https://aclanthology.org/C16-1102
%P 1071-1082

Markdown (Informal)

[Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization](https://aclanthology.org/C16-1102) (Zopf et al., COLING 2016)

• Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization (Zopf et al., COLING 2016)

ACL

• Markus Zopf, Eneldo Loza Mencía, and Johannes Fürnkranz. 2016. Sequential Clustering and Contextual Importance Measures for Incremental Update Summarization. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 1071–1082, Osaka, Japan. The COLING 2016 Organizing Committee.