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        <title>Real-Time News Summarization with Adaptation to Media Attention</title>
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            <namePart type="given">Ruslan</namePart>
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    <abstract>Real-time summarization of news events (RTS) allows persons to stay up-to-date on important topics that develop over time. With the occurrence of major sub-events, media attention increases and a large number of news articles are published. We propose a summarization approach that detects such changes and selects a suitable summarization configuration at run-time. In particular, at times with high media attention, our approach exploits the redundancy in content to produce a more precise summary and avoid emitting redundant information. We find that our approach significantly outperforms a strong non-adaptive RTS baseline in terms of the emitted summary updates and achieves the best results on a recent web-scale dataset. It can successfully be applied to a different real-world dataset without requiring additional modifications.</abstract>
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    <identifier type="doi">10.26615/978-954-452-049-6_079</identifier>
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%0 Conference Proceedings
%T Real-Time News Summarization with Adaptation to Media Attention
%A Rücklé, Andreas
%A Gurevych, Iryna
%Y Mitkov, Ruslan
%Y Angelova, Galia
%S Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
%D 2017
%8 September
%I INCOMA Ltd.
%C Varna, Bulgaria
%F ruckle-gurevych-2017-real
%X Real-time summarization of news events (RTS) allows persons to stay up-to-date on important topics that develop over time. With the occurrence of major sub-events, media attention increases and a large number of news articles are published. We propose a summarization approach that detects such changes and selects a suitable summarization configuration at run-time. In particular, at times with high media attention, our approach exploits the redundancy in content to produce a more precise summary and avoid emitting redundant information. We find that our approach significantly outperforms a strong non-adaptive RTS baseline in terms of the emitted summary updates and achieves the best results on a recent web-scale dataset. It can successfully be applied to a different real-world dataset without requiring additional modifications.
%R 10.26615/978-954-452-049-6_079
%U https://doi.org/10.26615/978-954-452-049-6_079
%P 610-617

Markdown (Informal)

[Real-Time News Summarization with Adaptation to Media Attention](https://doi.org/10.26615/978-954-452-049-6_079) (Rücklé & Gurevych, RANLP 2017)

• Real-Time News Summarization with Adaptation to Media Attention (Rücklé & Gurevych, RANLP 2017)

ACL

• Andreas Rücklé and Iryna Gurevych. 2017. Real-Time News Summarization with Adaptation to Media Attention. In Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017, pages 610–617, Varna, Bulgaria. INCOMA Ltd..