In our research we have created a text summarization software tool for
Hungarian using multilingual and Hungarian BERT-based models. Two types
of text summarization method exist: abstractive and extractive. The abstractive
summarization is more similar to human generated summarization.
Target summaries may include phrases that the original text does not necessarily
contain. This method generates the summarized text by applying
keywords that were extracted from the original text. The extractive method
summarizes the text by using the most important extracted phrases or sentences
from the original text. In our research we have built both abstractive
and extractive models for Hungarian. For abstractive models, we have used
a multilingual BERT model and Hungarian monolingual BERT models. For
extractive summarization, in addition to the BERT models, we have also
made experiments with ELECTRA models. We find that the Hungarian
monolingual models outperformed the multilingual BERT model in all cases.
Furthermore, the ELECTRA small models achieved higher results than some
of the BERT models. This result is important because the ELECTRA small
models have much fewer parameters and were trained on only 1 GPU within
a couple of days. Another important consideration is that the ELECTRA models are much smaller than the BERT models, which is important for the
end users. To our best knowledge the first extractive and abstractive summarization
systems reported in the present paper are the first such systems
for Hungarian