Neural sentence fusion for diversity driven abstractive multi-document summarization

Fuad, Tanvir Ahmed; Nayeem, Mir Tafseer; Mahmud, Asif; Chali, Yllias

doi:10.1016/j.csl.2019.04.006

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…These property inconsistencies will increase the difficulty of network learning. Confronting the nonuniform properties of different modules, the fusion analysis cannot be performed by simply listing them together, while some advanced fusion techniques are required [ 21 ]. The data belonging to each module has its own property, then a network weighted parameter is generated to adjust the input information of different modules.…”

Section: Introductionmentioning

confidence: 99%

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Fusion Evaluation of College Cultivation by Adaptive Multivariate Neural Network Model

Hong

Yang

et al. 2022

Computational Intelligence and Neuroscience

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The quality of graduates is the key factor in evaluating the cultivation effect of colleges and universities. Quantification of whether the graduates qualify for their working post in companies and industries provides conduction for further college cultivation reform enhancement. In this work, we proposed an adaptive multivariate neural network architecture for fusion evaluation of college student cultivation. Specifically, we designed a questionnaire to collect data on the current working status of 1231 graduates and recorded 32 in-school training items categorized into four different modules. For quantitative evaluation, 10 indices of career-require competence were set to describe the graduates’ job abilities. The fused contribution of the in-school training items to the career-required competence was predicted by the multivariate network model with the linking weights adaptively trained. A comprehensive contribution matrix was generated by discrete PCA multivariate transforming to provide a digital reference for the network training. A 7-level scoring system was designed for quantifying the contribution matrix. For model optimization, the network structure was tuned by testing a different number of hidden nodes. The model was trained and optimized to reveal the direct correlation between college cultivation and job-required abilities. Experimental results indicated that the methodology we proposed is feasible to evaluate the cultivation mode in colleges and universities, theoretically and technically providing positive directions for colleges and universities to make their cultivation reforming, as to enhance the quality of their graduates.

show abstract

Section: Introductionmentioning

confidence: 99%

“…ese property inconsistencies will increase the difficulty of network learning. Confronting the nonuniform properties of different modules, the fusion analysis cannot be performed by simply listing them together, while some advanced fusion techniques are required [21].…”

Section: Introductionmentioning

confidence: 99%

Fusion Evaluation of College Cultivation by Adaptive Multivariate Neural Network Model

Hong

Yang

et al. 2022

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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“…Transformer, as base language models, has significantly impacted the NLP research field to replace the deficiency of both LSTM, CNN and RNN based as a deep learning architecture [12,13], so that many reasons why the transformer was chosen as base model architecture. Various studies applied to transformer architecture have been carried out and have improved results significantly in document summarization [14].…”

Section: Introductionmentioning

confidence: 99%

Summarization of COVID-19 news documents deep learning-based using transformer architecture

2021

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Facing the news on the internet about the spreading of Corona virus disease 2019 (COVID-19) is challenging because it is required a long time to get valuable information from the news. Deep learning has a significant impact on NLP research. However, the deep learning models used in several studies, especially in document summary, still have a deficiency. For example, the maximum output of long text provides incorrectly. The other results are redundant, or the characters repeatedly appeared so that the resulting sentences were less organized, and the recall value obtained was low. This study aims to summarize using a deep learning model implemented to COVID-19 news documents. We proposed transformer as base language models with architectural modification as the basis for designing the model to improve results significantly in document summarization. We make a transformer-based architecture model with encoder and decoder that can be done several times repeatedly and make a comparison of layer modifications based on scoring. From the resulting experiment used, ROUGE-1 and ROUGE-2 show the good performance for the proposed model with scores 0.58 and 0.42, respectively, with a training time of 11438 seconds. The model proposed was evidently effective in improving result performance in abstractive document summarization.

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Abstractive Multi-document Summarization Using Deep Learning Approaches

Poornima

Pulipati

Kumar

2022

Proceedings of Second International Conference on Advances in Computer Engineering and Communication Systems

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Neural sentence fusion for diversity driven abstractive multi-document summarization

Cited by 12 publications

References 7 publications

Fusion Evaluation of College Cultivation by Adaptive Multivariate Neural Network Model

Fusion Evaluation of College Cultivation by Adaptive Multivariate Neural Network Model

Summarization of COVID-19 news documents deep learning-based using transformer architecture

Abstractive Multi-document Summarization Using Deep Learning Approaches

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