Real-Time News Summarization with Adaptation to Media Attention

Andreas Rücklé, Iryna Gurevych


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.
Anthology ID:
R17-1079
Volume:
Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017
Month:
September
Year:
2017
Address:
Varna, Bulgaria
Venue:
RANLP
SIG:
Publisher:
INCOMA Ltd.
Note:
Pages:
610–617
Language:
URL:
https://doi.org/10.26615/978-954-452-049-6_079
DOI:
10.26615/978-954-452-049-6_079
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PDF:
https://doi.org/10.26615/978-954-452-049-6_079