| dc.description.abstract | 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. | en_US |