Abstract
Text summarization is considered as a challenging task in the NLP community. The availability of datasets for the task of multilingual text summarization is rare, and such datasets are difficult to construct. In this work, we build an abstract text summarizer for the German language text using the state-of-the-art “Transformer” model. We propose an iterative data augmentation approach which uses synthetic data along with the real summarization data for the German language. To generate synthetic data, the Common Crawl (German) dataset is exploited, which covers different domains. The synthetic data is effective for the low resource condition and is particularly helpful for our multilingual scenario where availability of summarizing data is still a challenging issue. The data are also useful in deep learning scenarios where the neural models require a large amount of training data for utilization of its capacity. The obtained summarization performance is measured in terms of ROUGE and BLEU score. We achieve an absolute improvement of +1.5 and +16.0 in ROUGE1 F1 (R1_F1) on the development and test sets, respectively, compared to the system which does not rely on data augmentation.- Anthology ID:
- D19-1616
- Volume:
- Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
- Month:
- November
- Year:
- 2019
- Address:
- Hong Kong, China
- Editors:
- Kentaro Inui, Jing Jiang, Vincent Ng, Xiaojun Wan
- Venues:
- EMNLP | IJCNLP
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 5994–5998
- Language:
- URL:
- https://aclanthology.org/D19-1616
- DOI:
- 10.18653/v1/D19-1616
- Cite (ACL):
- Shantipriya Parida and Petr Motlicek. 2019. Abstract Text Summarization: A Low Resource Challenge. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5994–5998, Hong Kong, China. Association for Computational Linguistics.
- Cite (Informal):
- Abstract Text Summarization: A Low Resource Challenge (Parida & Motlicek, EMNLP-IJCNLP 2019)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/D19-1616.pdf