A New Dataset and Efficient Baselines for Document-level Text Simplification in German
Annette Rios | Nicolas Spring | Tannon Kew | Marek Kostrzewa | Andreas Säuberli | Mathias Müller | Sarah Ebling
Proceedings of the Third Workshop on New Frontiers in Summarization
The task of document-level text simplification is very similar to summarization with the additional difficulty of reducing complexity. We introduce a newly collected data set of German texts, collected from the Swiss news magazine 20 Minuten (‘20 Minutes’) that consists of full articles paired with simplified summaries. Furthermore, we present experiments on automatic text simplification with the pretrained multilingual mBART and a modified version thereof that is more memory-friendly, using both our new data set and existing simplification corpora. Our modifications of mBART let us train at a lower memory cost without much loss in performance, in fact, the smaller mBART even improves over the standard model in a setting with multiple simplification levels.
A Corpus for Automatic Readability Assessment and Text Simplification of German
Alessia Battisti | Dominik Pfütze | Andreas Säuberli | Marek Kostrzewa | Sarah Ebling
Proceedings of the Twelfth Language Resources and Evaluation Conference
In this paper, we present a corpus for use in automatic readability assessment and automatic text simplification for German, the first of its kind for this language. The corpus is compiled from web sources and consists of parallel as well as monolingual-only (simplified German) data amounting to approximately 6,200 documents (nearly 211,000 sentences). As a unique feature, the corpus contains information on text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and images (content, position, and dimensions). While the importance of considering such information in machine learning tasks involving simplified language, such as readability assessment, has repeatedly been stressed in the literature, we provide empirical evidence for its benefit. We also demonstrate the added value of leveraging monolingual-only data for automatic text simplification via machine translation through applying back-translation, a data augmentation technique.
- Andreas Säuberli 2
- Sarah Ebling 2
- Annette Rios Gonzales 1
- Nicolas Spring 1
- Tannon Kew 1
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