@InProceedings{ruckle-gurevych:2017:RANLP,
  author    = {R\"{u}ckl\'{e}, Andreas  and  Gurevych, Iryna},
  title     = {Real-Time News Summarization with Adaptation to Media Attention},
  booktitle = {Proceedings of the International Conference Recent Advances in Natural Language Processing, RANLP 2017},
  month     = {September},
  year      = {2017},
  address   = {Varna, Bulgaria},
  publisher = {INCOMA Ltd.},
  pages     = {610--617},
  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.},
  url       = {https://doi.org/10.26615/978-954-452-049-6_079}
}