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Keyword: abstractive summarization ×Clear all

2 claims from 1 paper

1. Computer Science › Topic Modeling

   Unified Language Model Pre-training for Natural Language Understanding and Generation

   Dong, Yang, Wang et al. · arXiv (Cornell University) · 2019

   The paper presents UniLM, a single pre-trained language model that can be fine-tuned for both language understanding and generation tasks, using three kinds of pre-training task.

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   1. Unchecked“UniLM compares favorably with BERT on the GLUE benchmark, and the SQuAD 2.0 and CoQA question answering tasks.”
   2. UncheckedUniLM is reported to set new state-of-the-art results on five text generation datasets, including summarisation, question answering and dialogue.“Moreover, UniLM achieves new state-of-the-art results on five natural language generation datasets, including improving the CNN/DailyMail abstractive summarization ROUGE-L to 40.51 (2.04 absolute improvement), the Gigaword abstractive summarization ROUGE-L to 35.75 (0.86 absolute improvement), the…”