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Erschienen in:
Radical Enhanced Chinese Word Embedding Kapitel lesen Erstes Kapitel lesen

2018 | OriginalPaper | Buchkapitel

A Hierarchical Hybrid Neural Network Architecture for Chinese Text Summarization

verfasst von : Yunheng Zhang, Leihan Zhang, Ke Xu, Le Zhang

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

Using sequence-to-sequence models for abstractive text summarization is generally plagued by three problems: inability to deal with out-of-vocabulary words, repetition in summaries and time-consuming in training. The paper proposes a hierarchical hybrid neural network architecture for Chinese text summarization. Three mechanisms, hierarchical attention mechanism, pointer mechanism and coverage mechanism, are integrated into the architecture to improve the performance of summarization. The proposed model is applied to Chinese news headline generation. The experimental results suggest that the model outperforms the baseline in ROUGE scores and the three mechanisms can improve the quality of summaries.

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Vorheriges Kapitel Multi-Perspective Fusion Network for Commonsense Reading Comprehension
Nächstes Kapitel TSABCNN: Two-Stage Attention-Based Convolutional Neural Network for Frame Identification
Fußnoten
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Metadaten
Titel
A Hierarchical Hybrid Neural Network Architecture for Chinese Text Summarization
verfasst von
Yunheng Zhang
Leihan Zhang
Ke Xu
Le Zhang
Copyright-Jahr
2018
Buch
Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Print ISBN: 978-3-030-01715-6

Electronic ISBN: 978-3-030-01716-3

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01716-3_23