Create README.md
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README.md
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[Paper](https://hlt.bme.hu/en/publ/foszt2oszt)
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We publish an abstractive summarizer for Hungarian, an
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encoder-decoder model initialized with [huBERT](huggingface.co/SZTAKI-HLT/hubert-base-cc), and fine-tuned on the
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[ELTE.DH](https://elte-dh.hu/) corpus of former Hungarian news portals. The model produces fluent output in the correct topic, but it hallucinates frequently.
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Our quantitative evaluation on automatic and human transcripts of news
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(with automatic and human-made punctuation, [Tündik et al. (2019)](https://www.isca-speech.org/archive/interspeech_2019/tundik19_interspeech.html), [Tündik and Szaszák (2019)](https://www.isca-speech.org/archive/interspeech_2019/szaszak19_interspeech.html)) shows that the model is
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robust with respect to errors in either automatic speech recognition or
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automatic punctuation restoration.
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