Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling-Reference-Cited by-同舟云学术

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Unsupervised neural networks for automatic Arabic text summarization using document clustering and topic modeling

Published:2021-06 Issue: Volume:172 Page:114652
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Alami Nabil,Meknassi Mohammed,En-nahnahi Noureddine,El Adlouni Yassine,Ammor Ouafae

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference61 articles.

1. Spectral clustering via ensemble deep autoencoder learning (SC-EDAE);Affeldt;Pattern Recognition,2020

2. Model-based co-clustering for the effective handling of sparse data;Ailem;Pattern Recognition,2017

3. Sparse poisson latent block model for document clustering;Ailem;IEEE Transactions on Knowledge and Data Engineering,2017

4. Using unsupervised deep learning for automatic summarization of Arabic documents;Alami;Arabian Journal for Science and Engineering,2018

5. Enhancing unsupervised neural networks based text summarization with word embedding and ensemble learning;Alami;Expert Systems with Applications,2019

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2. Extractive Arabic Text Summarization Using PageRank and Word Embedding;Arabian Journal for Science and Engineering;2024-04-18

3. Review on Text Summarization using Clustering and Machine Learning-Deep Learning Models;2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC - ROBINS);2024-04-17

4. TxLASM: A novel language agnostic summarization model for text documents;Expert Systems with Applications;2024-03

5. Extraction of time-related expressions using text mining with application to Hebrew;PLOS ONE;2024-02-23

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