Abstract Text Summarization: A Low Resource Challenge

By Shantipriya Parida, Petr Motlícek. 2019

46 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Management Science and Operations Research, Summarization

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

  1. Heike Adel
  2. Jannik Strotgen
  3. Dietrich Klakow
  4. Lukas Lange
  5. Michael A. Hedderich
  6. Christopher R'e
  7. Zhijian Ou
  8. Mengqiu Wang

In Management Science and Operations Research

  1. Dietrich Klakow

Citations in the corpus