Comprehensive Document Summarization with Refined Self-Matching Mechanism
Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the enc...
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doaj-3ca05b7310bb4cc39184fad63c0a6fbf2020-11-25T03:01:45ZengMDPI AGApplied Sciences2076-34172020-03-01105186410.3390/app10051864app10051864Comprehensive Document Summarization with Refined Self-Matching MechanismBiqing Zeng0Ruyang Xu1Heng Yang2Zibang Gan3Wu Zhou4School of Software, South China Normal University, Foshan 528225, ChinaSchool of Computer, South China Normal University, Guangzhou 510631, ChinaSchool of Computer, South China Normal University, Guangzhou 510631, ChinaSchool of Software, South China Normal University, Foshan 528225, ChinaSchool of Computer, South China Normal University, Guangzhou 510631, ChinaUnder the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the encoder to optimize the encoding information at the global level and effectively improves the memory capacity of conventional LSTM. Inspired by human coarse-to-fine understanding mode, localness is modeled by Gaussian bias to improve contextualization for each sentence, and merged into the self-matching energy. The refined self-matching mechanism not only establishes global document attention but perceives association with neighboring signals. At the decoder side, the pointer network is utilized to perform a two-hop attention on context and extraction state. Evaluations on the CNN/Daily Mail dataset verify that the proposed model outperforms the strong baseline models and statistical significantly.https://www.mdpi.com/2076-3417/10/5/1864extractive summarizationdeep learningdocument summarization |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Biqing Zeng Ruyang Xu Heng Yang Zibang Gan Wu Zhou |
spellingShingle |
Biqing Zeng Ruyang Xu Heng Yang Zibang Gan Wu Zhou Comprehensive Document Summarization with Refined Self-Matching Mechanism Applied Sciences extractive summarization deep learning document summarization |
author_facet |
Biqing Zeng Ruyang Xu Heng Yang Zibang Gan Wu Zhou |
author_sort |
Biqing Zeng |
title |
Comprehensive Document Summarization with Refined Self-Matching Mechanism |
title_short |
Comprehensive Document Summarization with Refined Self-Matching Mechanism |
title_full |
Comprehensive Document Summarization with Refined Self-Matching Mechanism |
title_fullStr |
Comprehensive Document Summarization with Refined Self-Matching Mechanism |
title_full_unstemmed |
Comprehensive Document Summarization with Refined Self-Matching Mechanism |
title_sort |
comprehensive document summarization with refined self-matching mechanism |
publisher |
MDPI AG |
series |
Applied Sciences |
issn |
2076-3417 |
publishDate |
2020-03-01 |
description |
Under the constraint of memory capacity of the neural network and the document length, it is difficult to generate summaries with adequate salient information. In this work, the self-matching mechanism is incorporated into the extractive summarization system at the encoder side, which allows the encoder to optimize the encoding information at the global level and effectively improves the memory capacity of conventional LSTM. Inspired by human coarse-to-fine understanding mode, localness is modeled by Gaussian bias to improve contextualization for each sentence, and merged into the self-matching energy. The refined self-matching mechanism not only establishes global document attention but perceives association with neighboring signals. At the decoder side, the pointer network is utilized to perform a two-hop attention on context and extraction state. Evaluations on the CNN/Daily Mail dataset verify that the proposed model outperforms the strong baseline models and statistical significantly. |
topic |
extractive summarization deep learning document summarization |
url |
https://www.mdpi.com/2076-3417/10/5/1864 |
work_keys_str_mv |
AT biqingzeng comprehensivedocumentsummarizationwithrefinedselfmatchingmechanism AT ruyangxu comprehensivedocumentsummarizationwithrefinedselfmatchingmechanism AT hengyang comprehensivedocumentsummarizationwithrefinedselfmatchingmechanism AT zibanggan comprehensivedocumentsummarizationwithrefinedselfmatchingmechanism AT wuzhou comprehensivedocumentsummarizationwithrefinedselfmatchingmechanism |
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1724692284935503872 |