A Study on Abstractive Summarization Techniques in Indian Languages

Sunitha, C.; Jaya, A.; Ganesh, Amal

doi:10.1016/j.procs.2016.05.121

Cited by 31 publications

(11 citation statements)

References 15 publications

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“…In the database service section, the project uses a SQLite 8 lightweight database for data persistence and easy deployment. The rendering is shown in Figure 7: The system implements the following functions: the administrator and user are the two main user interfaces of the platform 9 . The administrator side implements user management functions, while the user side provides multiple modules such as login and exit, homepage, title generation, summary generation, keyword extraction, word cloud display, sentiment analysis, text similarity analysis, and password modification 10 .…”

Section: Implementation Of Text Processing Systemmentioning

confidence: 99%

Research and implementation of title generation for news text

Li,

Tang,

Lin

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

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This paper explores the natural language processing technology of news text, especially the algorithm and application of title generation and abstraction. Based on the analysis of the existing principle of automatic title generation and abstraction, the data sets are preprocessed by text cleaning and word quantization according to the objective and accurate features of news text. Then, the Roberta, GPT-2, and T5-pegasus models were evaluated, and the method of model fusion and integration based on the principle of accuracy priority was proposed to improve the accuracy of title generation by 3%. Finally, the news text processing system is constructed based on the optimized model. Experimental results show that the proposed algorithm has high reliability and robustness. This topic has been applied, and the application shows that the results of the news text processing system are accurate and objective, and have high adaptability.

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Section: Implementation Of Text Processing Systemmentioning

confidence: 99%

Research and implementation of title generation for news text

Li,

Tang,

Lin

2023

Fifth International Conference on Artificial Intelligence and Computer Science (AICS 2023)

View full text Add to dashboard Cite

show abstract

“…Furthermore, abstractive summarization is more complicated than extractive summarization because abstractive summarization requires semantic analysis of the text that can be achieved by using machine learning techniques and advanced natural language processing (NLP) [ 4 ]. However, abstractive summarization is better, since it is like a summary that is written by humans, so it is more meaningful [ 5 ].…”

Section: Introductionmentioning

confidence: 99%

Abstractive Arabic Text Summarization Based on Deep Learning

Wazery

Saleh

Alharbi

et al. 2022

Computational Intelligence and Neuroscience

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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.

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“…Two broad areas of Natural Language Processing (NLP) [12] that handle abstractive summary are semantic representation and natural language generation [4]. These involve various approaches, such as multimodal semantic models, information item-based methods, and semantic graph-based methods [13]. An extractive summary is described as units of text extracted from the source document(s) and combined as a summary verbatim [14].…”

Section: Introductionmentioning

confidence: 99%

Extractive Multi-Document Summarization: A Review of Progress in the Last Decade

Jalil

Nasir

2021

IEEE Access

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With the tremendous growth in the number of electronic documents, it is becoming challenging to manage the volume of information. Much research has focused on automatically summarizing the information available in the documents. Multi-Document Summarization (MDS) is one approach that aims to extract the information from the available documents in such a concise way that none of the important points are missed from the summary while avoiding the redundancy of information at the same time. This study presents an extensive survey of extractive MDS over the last decade to show the progress of research in this field. We present different techniques of extractive MDS and compare their strengths and weaknesses. Research work is presented by category and evaluated to help the reader understand the work in this field and to guide them in defining their own research directions. Benchmark datasets and standard evaluation techniques are also presented. This study concludes that most of the extractive MDS techniques are successful in developing salient and information-rich summaries of the documents provided.

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A Study on Abstractive Summarization Techniques in Indian Languages

Cited by 31 publications

References 15 publications

Research and implementation of title generation for news text

Research and implementation of title generation for news text

Abstractive Arabic Text Summarization Based on Deep Learning

Extractive Multi-Document Summarization: A Review of Progress in the Last Decade

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