Research on Text Summarization Generation Based on LSTM and Attention Mechanism

2021 ◽  
Author(s):  
Meiqi Ji ◽  
Ruiling Fu ◽  
Tongtong Xing ◽  
Fulian Yin
Keyword(s):  
Attention Mechanism ◽  
Text Summarization

scholarly journals Abstractive Arabic Text Summarization Based on Deep Learning

2022 ◽  
Vol 2022 ◽  
pp. 1-14
Author(s):  
Y.M. Wazery ◽  
Marwa E. Saleh ◽  
Abdullah Alharbi ◽  
Abdelmgeid A. Ali
Keyword(s):  
Short Term Memory ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Arabic Text ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory ◽  
Arabic Text Summarization ◽  
Abstractive Summarization ◽  
Research Studies

Text summarization (TS) is considered one of the most difficult tasks in natural language processing (NLP). It is one of the most important challenges that stand against the modern computer system’s capabilities with all its new improvement. Many papers and research studies address this task in literature but are being carried out in extractive summarization, and few of them are being carried out in abstractive summarization, especially in the Arabic language due to its complexity. In this paper, an abstractive Arabic text summarization system is proposed, based on a sequence-to-sequence model. This model works through two components, encoder and decoder. Our aim is to develop the sequence-to-sequence model using several deep artificial neural networks to investigate which of them achieves the best performance. Different layers of Gated Recurrent Units (GRU), Long Short-Term Memory (LSTM), and Bidirectional Long Short-Term Memory (BiLSTM) have been used to develop the encoder and the decoder. In addition, the global attention mechanism has been used because it provides better results than the local attention mechanism. Furthermore, AraBERT preprocess has been applied in the data preprocessing stage that helps the model to understand the Arabic words and achieves state-of-the-art results. Moreover, a comparison between the skip-gram and the continuous bag of words (CBOW) word2Vec word embedding models has been made. We have built these models using the Keras library and run-on Google Colab Jupiter notebook to run seamlessly. Finally, the proposed system is evaluated through ROUGE-1, ROUGE-2, ROUGE-L, and BLEU evaluation metrics. The experimental results show that three layers of BiLSTM hidden states at the encoder achieve the best performance. In addition, our proposed system outperforms the other latest research studies. Also, the results show that abstractive summarization models that use the skip-gram word2Vec model outperform the models that use the CBOW word2Vec model.

scholarly journals Text Summarization using Extractive and Abstractive Methods

2021 ◽  
Vol 40 ◽  
pp. 03023
Author(s):  
Saurabh Varade ◽  
Ejaaz Sayyed ◽  
Vaibhavi Nagtode ◽  
Shilpa Shinde
Keyword(s):  
Input Sequence ◽  
Link Analysis ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Ranking Algorithm ◽  
Text File ◽  
Extractive Summarization ◽  
Ranking Algorithms ◽  
Original Meaning ◽  
Abstractive Summarization

Text Summarization is a process where a huge text file is converted into summarized version which will preserve the original meaning and context. The main aim of any text summarization is to provide a accurate and precise summary. One approach is to use a sentence ranking algorithm. This comes under extractive summarization. Here, a graph based ranking algorithm is used to rank the sentences in the text and then top k-scored sentences are included in the summary. The most widely used algorithm to decide the importance of any vertex in a graph based on the information retrieved from the graph is Graph Based Ranking Algorithm. TextRank is one of the most efficient ranking algorithms which is used for Web link analysis that is for measuring the importance of website pages. Another approach is abstractive summarization where a LSTM encoder decoder model is used along with attention mechanism which focuses on some important words from the input. Encoder encodes the input sequence and decoder along with attention mechanism gives the summary as the output.

Unsupervised Multi-Document Abstractive Summarization Using Recursive Neural Network with Attention Mechanism

2020 ◽  
Vol 17 (9) ◽  
pp. 3867-3872
Author(s):  
Aniv Chakravarty ◽  
Jagadish S. Kallimani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Text Generation ◽  
Text Documents ◽  
Current State ◽  
Semantic Concepts ◽  
Text Information ◽  
Abstractive Summarization

Text summarization is an active field of research with a goal to provide short and meaningful gists from large amount of text documents. Extractive text summarization methods have been extensively studied where text is extracted from the documents to build summaries. There are various type of multi document ranging from different formats to domains and topics. With the recent advancement in technology and use of neural networks for text generation, interest for research in abstractive text summarization has increased significantly. The use of graph based methods which handle semantic information has shown significant results. When given a set of documents of English text files, we make use of abstractive method and predicate argument structures to retrieve necessary text information and pass it through a neural network for text generation. Recurrent neural networks are a subtype of recursive neural networks which try to predict the next sequence based on the current state and considering the information from previous states. The use of neural networks allows generation of summaries for long text sentences as well. This paper implements a semantic based filtering approach using a similarity matrix while keeping all stop-words. The similarity is calculated using semantic concepts and Jiang–Conrath similarity and making use of a recurrent neural network with an attention mechanism to generate summary. ROUGE score is used for measuring accuracy, precision and recall scores.

scholarly journals Abstract Text Summarization with a Convolutional Seq2seq Model

2019 ◽  
Vol 9 (8) ◽  
pp. 1665 ◽  
Author(s):  
Yong Zhang ◽  
Dan Li ◽  
Yuheng Wang ◽  
Yang Fang ◽  
Weidong Xiao
Keyword(s):  
State Of The Art ◽  
Real Life ◽  
Generative Model ◽  
Attention Mechanism ◽  
Text Summarization ◽  
Model Based ◽  
Main Ideas

Abstract text summarization aims to offer a highly condensed and valuable information that expresses the main ideas of the text. Most previous researches focus on extractive models. In this work, we put forward a new generative model based on convolutional seq2seq architecture. A hierarchical CNN framework is much more efficient than the conventional RNN seq2seq models. We also equip our model with a copying mechanism to deal with the rare or unseen words. Additionally, we incorporate a hierarchical attention mechanism to model the keywords and key sentences simultaneously. Finally we verify our model on two real-life datasets, GigaWord and DUC corpus. The experiment results verify the effectiveness of our model as it outperforms state-of-the-art alternatives consistently and statistical significantly.

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