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        <title>A Temporally Sensitive Submodularity Framework for Timeline Summarization</title>
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            <title>Proceedings of the 22nd Conference on Computational Natural Language Learning</title>
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            <namePart type="given">Anna</namePart>
            <namePart type="family">Korhonen</namePart>
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        <name type="personal">
            <namePart type="given">Ivan</namePart>
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    <abstract>Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield well-performing TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack.</abstract>
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    <identifier type="doi">10.18653/v1/K18-1023</identifier>
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%0 Conference Proceedings
%T A Temporally Sensitive Submodularity Framework for Timeline Summarization
%A Martschat, Sebastian
%A Markert, Katja
%Y Korhonen, Anna
%Y Titov, Ivan
%S Proceedings of the 22nd Conference on Computational Natural Language Learning
%D 2018
%8 October
%I Association for Computational Linguistics
%C Brussels, Belgium
%F martschat-markert-2018-temporally
%X Timeline summarization (TLS) creates an overview of long-running events via dated daily summaries for the most important dates. TLS differs from standard multi-document summarization (MDS) in the importance of date selection, interdependencies between summaries of different dates and by having very short summaries compared to the number of corpus documents. However, we show that MDS optimization models using submodular functions can be adapted to yield well-performing TLS models by designing objective functions and constraints that model the temporal dimension inherent in TLS. Importantly, these adaptations retain the elegance and advantages of the original MDS models (clear separation of features and inference, performance guarantees and scalability, little need for supervision) that current TLS-specific models lack.
%R 10.18653/v1/K18-1023
%U https://aclanthology.org/K18-1023
%U https://doi.org/10.18653/v1/K18-1023
%P 230-240

Markdown (Informal)

[A Temporally Sensitive Submodularity Framework for Timeline Summarization](https://aclanthology.org/K18-1023) (Martschat & Markert, CoNLL 2018)

• A Temporally Sensitive Submodularity Framework for Timeline Summarization (Martschat & Markert, CoNLL 2018)

ACL

• Sebastian Martschat and Katja Markert. 2018. A Temporally Sensitive Submodularity Framework for Timeline Summarization. In Proceedings of the 22nd Conference on Computational Natural Language Learning, pages 230–240, Brussels, Belgium. Association for Computational Linguistics.