Abstract
The recent researches demonstrate the methods of generating text headline can tackle the problem of information overload. However, the popular neural network based framework, namely, encoder-decoder framework is difficult to handle the headline generation task of long source text. In this paper, we establish a hierarchical attention headline generation model with a filter to solve this problem. This model first relies on a filter to extract crucial contents of the source text, and then the hierarchical attention mechanism accurately identifies important words. Finally, our model generates a high-quality headline. Experimental results show that the ROUGE scores of our model are higher than those of classical models. Furthermore, our model achieves better performance than classical model in dealing with long text.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China [Grant Nos. 61872228].
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Xie, J., Wang, X., Wang, X., Pang, G. (2020). A Hierarchical Attention Headline Generation Model with a Filter. In: Park, J., Yang, L., Jeong, YS., Hao, F. (eds) Advanced Multimedia and Ubiquitous Engineering. MUE FutureTech 2019 2019. Lecture Notes in Electrical Engineering, vol 590. Springer, Singapore. https://doi.org/10.1007/978-981-32-9244-4_47
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DOI: https://doi.org/10.1007/978-981-32-9244-4_47
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