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7 March 2022 Chinese text summarization based on fine-tuned GPT2
Qihang Zhu, Lin Li, Libing Bai, Feng Hu
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Proceedings Volume 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021); 121671A (2022) https://doi.org/10.1117/12.2629132
Event: 2021 Third International Conference on Electronics and Communication, Network and Computer Technology, 2021, Harbin, China
Abstract
Pre-trained language model has a good performance in text summarization task, thus we present a neural text summarization based on a powerful pre-trained language model GPT-2. In this paper, we propose a Chinese text summarization model by extending into our downstream task to acquire relevant, contentful, and coherent summarization. By extensive experiments, our model achieves absolute improvements of 10.75% on ROUGE-1, 13.85% on ROUGE-2, and 9.73% on ROUGE-L on the LCSTS datasets. Compared with the state-of-the-art summarization model, e.g. BERTSUM based model, our model also achieves an improvement of 25.22% on ROUGE-1.
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Qihang Zhu, Lin Li, Libing Bai, and Feng Hu "Chinese text summarization based on fine-tuned GPT2", Proc. SPIE 12167, Third International Conference on Electronics and Communication; Network and Computer Technology (ECNCT 2021), 121671A (7 March 2022); https://doi.org/10.1117/12.2629132
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KEYWORDS
Performance modeling
Data modeling
Neural networks
Data processing
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