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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/84039

    Title: 結合擷取式與萃取式兩段式模型以增進摘要效能之研究
    Authors: 王美淋;Wang, Mei-Lin
    Contributors: 資訊管理學系
    Keywords: 中文萃取式摘要;自然語言處理;遞迴神經網路;詞向量;Transformer;Chinese Abstractive Summarization;NLP;Recurrent neural network;word embedding;Transformer
    Date: 2020-07-20
    Issue Date: 2020-09-02 17:57:52 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 有鑑於現今網路資源豐富,人們無法短時間內處理這些龐大的資料,自動文本摘要任務因應而生。本研究使用兩段式模型,結合擷取式模型與萃取式模型,利用額外的輸入與預訓練模型,使萃取式模型產生通順的中文摘要結果,提升自動文本摘要之效能,並在其中探討詞頻模型與深度學習模型,應用於擷取式模型之好壞,以及擷取式模型輸出詞級別與句級別對於萃取式模型之影響。
    ;In an era of information expansion, it brings abundant internet resources for human. But it is difficult for people to handle these huge data in a short time, hence the automatic text summarization task has been proposed accordingly. This research applies a two-stage model to do abstractive summarization task, combining an extractive model and an extractive model, and uses additional inputs and pre-trained models to make the abstractive model produce logical and semantically-smooth Chinese summary to improve the performance of automatic text summarization. We also discuss the word frequency model and deep learning model, which applied to the extraction model is best, and the impact of word level and sentence level output on the extraction model.
    According to the experimental results, we found that the two-stage model proposed in this research is better than the Transformer on the experimental performance, and the performance of Rouge-1 and Rouge-2 is State-of-the-Art better than models proposed by other scholars.
    The best model uses the TF-IDF and the word-level output method on the first-stage extraction model. Rouge-1, Rouge-2 and Rouge-L are 0.447、0.268 and 0.407.
    It also has a good performance with deep learning model in the extractive stage. In this situation, the best result is to use a three-layer BiLSTM with attention mechanism. The results of Rouge-1, Rouge-2 and Rouge-L are 0.4435, 0.2669 and 0.4, which is not far from the performance of TF-IDF.
    Therefore we can infer that we proposes a system that can effectively improve the performance of Chinese summarization for abstractive generation tasks and helps follow-up scholars to do the related researches there are grounds to follow.
    Appears in Collections:[資訊管理研究所] 博碩士論文

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