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A Hierarchical Attention Headline Generation Model with a Filter

  • 2020
  • OriginalPaper
  • Chapter
Published in:
Authors
Jiehang Xie
Xiaoming Wang
Xinyan Wang
Guangyao Pang
Published in
Advanced Multimedia and Ubiquitous Engineering
Publisher
Springer Singapore

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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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previous chapter Optimal Control of the Adversarial Information Propagation in Online Social Networks
next chapter An ICP-Based Point Clouds Registration Method for Indoor Environment Modeling
Title
A Hierarchical Attention Headline Generation Model with a Filter
Authors
Jiehang Xie
Xiaoming Wang
Xinyan Wang
Guangyao Pang
Copyright Year
2020
Publisher
Springer Singapore
Book
Advanced Multimedia and Ubiquitous Engineering

Print ISBN: 978-981-329-243-7

Electronic ISBN: 978-981-329-244-4

Copyright Year: 2020

DOI
https://doi.org/10.1007/978-981-32-9244-4_47
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