Abstract
In this paper we propose a process to extract summarization corpora from Wikipedia articles. Applied to the German language we create a corpus of 240,000 texts. We use ROUGE scores for the extraction and evaluation of our corpus. For this we provide a ROUGE metric implementation adapted to the German language. The extracted corpus is used to train three abstractive summarization models which we compare to different baselines. The resulting summaries sound natural and cover the input text very well. The corpus can be downloaded at https://github.com/domfr/GeWiki.- Anthology ID:
- 2020.lrec-1.821
- Volume:
- Proceedings of the Twelfth Language Resources and Evaluation Conference
- Month:
- May
- Year:
- 2020
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6651–6655
- Language:
- English
- URL:
- https://aclanthology.org/2020.lrec-1.821
- DOI:
- Cite (ACL):
- Dominik Frefel. 2020. Summarization Corpora of Wikipedia Articles. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 6651–6655, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Summarization Corpora of Wikipedia Articles (Frefel, LREC 2020)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-2/2020.lrec-1.821.pdf