Rücklé, Andreas ; Gurevych, Iryna (2017)
Real-Time News Summarization with Adaptation to Media Attention.
Varna, Bulgaria
doi: 10.26615/978-954-452-049-6_079
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Rücklé, Andreas ; Gurevych, Iryna |
| Art des Eintrags: | Bibliographie |
| Titel: | Real-Time News Summarization with Adaptation to Media Attention |
| Sprache: | Englisch |
| Publikationsjahr: | September 2017 |
| Verlag: | Association for Computational Linguistics |
| Buchtitel: | Proceedings of the 11th Conference on Recent Advances in Natural Language Processing (RANLP 2017) |
| Veranstaltungsort: | Varna, Bulgaria |
| DOI: | 10.26615/978-954-452-049-6_079 |
| URL / URN: | https://doi.org/10.26615/978-954-452-049-6_079 |
| Kurzbeschreibung (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. |
| Freie Schlagworte: | UKP_p_QAEduInf |
| ID-Nummer: | TUD-CS-2017-0181 |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Ubiquitäre Wissensverarbeitung |
| Hinterlegungsdatum: | 11 Jul 2017 19:26 |
| Letzte Änderung: | 24 Jan 2020 12:03 |
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