Improving abstractive summarization of legal rulings through textual entailment

2021 ◽  
Author(s):  
Diego de Vargas Feijo ◽  
Viviane P. Moreira
Keyword(s):  
Textual Entailment ◽  
Abstractive Summarization

Hits-based attentional neural model for abstractive summarization

2021 ◽  
pp. 106996
Author(s):  
Xiaoyan Cai ◽  
Kaile Shi ◽  
Yuehan Jiang ◽  
Libin Yang ◽  
Sen Liu
Keyword(s):  
Neural Model ◽  
Abstractive Summarization

Improving Abstractive Summarization Based on Dynamic Residual Network with Reinforce Dependency

2021 ◽  
Author(s):  
Weizhi Liao ◽  
Yaheng Ma ◽  
Yanchao Yin ◽  
Guanglei Ye ◽  
Dongzhou Zuo
Keyword(s):  
Residual Network ◽  
Abstractive Summarization

scholarly journals ASoVS: Abstractive Summarization of Video Sequences

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 29253-29263 ◽  
Author(s):  
Aniqa Dilawari ◽  
Muhammad Usman Ghani Khan
Keyword(s):  
Video Sequences ◽  
Abstractive Summarization

SGAN4AbSum: A Semantic-Enhanced Generative Adversarial Network for Abstractive Text Summarization

2021 ◽  
Author(s):  
Tham Vo
Keyword(s):  
Ground Truth ◽  
Text Summarization ◽  
Generative Adversarial Network ◽  
Convolutional Network ◽  
Training Strategy ◽  
Adversarial Network ◽  
Deep Recurrent Neural Network ◽  
Benchmark Datasets ◽  
Latent Representations ◽  
Abstractive Summarization

Abstract In abstractive summarization task, most of proposed models adopt the deep recurrent neural network (RNN)-based encoder-decoder architecture to learn and generate meaningful summary for a given input document. However, most of recent RNN-based models always suffer the challenges related to the involvement of much capturing high-frequency/reparative phrases in long documents during the training process which leads to the outcome of trivial and generic summaries are generated. Moreover, the lack of thorough analysis on the sequential and long-range dependency relationships between words within different contexts while learning the textual representation also make the generated summaries unnatural and incoherent. To deal with these challenges, in this paper we proposed a novel semantic-enhanced generative adversarial network (GAN)-based approach for abstractive text summarization task, called as: SGAN4AbSum. We use an adversarial training strategy for our text summarization model in which train the generator and discriminator to simultaneously handle the summary generation and distinguishing the generated summary with the ground-truth one. The input of generator is the jointed rich-semantic and global structural latent representations of training documents which are achieved by applying a combined BERT and graph convolutional network (GCN) textual embedding mechanism. Extensive experiments in benchmark datasets demonstrate the effectiveness of our proposed SGAN4AbSum which achieve the competitive ROUGE-based scores in comparing with state-of-the-art abstractive text summarization baselines.

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