A Faster Method For Generating Chinese Text Summaries-Combining Extractive Summarization And Abstractive Summarization

MLNLP(2022)

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摘要
Extractive summarization and generative summarization are the two main ways to generate summarization. However,previous work treats both of them as two independent subtasks. In this paper,we obtain new summarization by combining extractive summarization and generative summarization. This method extracts the key information of the article firstly,and then generates the summarization of the extracted information. The experimental result shows that this method can significantly improve the quality of the generative text compared with extractive summarization,and can significantly improve the generative speed compared with generative summarization.
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关键词
Abstractive Summarization, Generative Summarization, T5 Model, Nezha Model, Recall-Oriented Understudy for Gisting Evaluation
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