Learning to predict text quality using Generative Adversarial Networks
Generating summaries of long text articles is a common application in natural language processing. Automatic text summarization models often find themselves generating summaries that don’t resemble the quality of human written text, even though they preserve factual accuracy. In this thesis, a metho...
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| Format: | Others |
| Language: | English |
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KTH, Numerisk analys, NA
2019
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-264109 |