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