Abstractive Text Summarization on Google Search Results

Patel, Dikshita; Shah, Nisarg; Shah, Vrushali Bhushan; Hole, Varsha

doi:10.1109/iciccs48265.2020.9120998

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…They built dictionary for word embedding, abstractive outline created utilizing the sequence-to-sequence model which utilization Long Short-Term Memory (LSTM) encoding decoding for preparation those model to process the result. Three stacked LSTM may be assembled for that encoder [8].…”

Section: Related Workmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Text Lecture Summarization

2022

IJCCCE

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Text summarization can be utilized for variety type of purposes; one of them for summary lecture file. A long document expended long time and large capacity. Since it may contain duplicated information, more over, irrelevant details that take long period to access relevant information. Summarization is a technique which provides the primary points of the whole document, and in the same time it will indicates the majority of the information in a small amount of time. For this reason it can save user time, decrease storage, and increase transfer speed to transmit through the internet. The summarization process will eliminate duplicated data, unimportant information, and also replace complex expression with simpler expression. The proposed method is using convolutional recurrent neural network deep model as a method for abstractive text summarization of lecture file that will be great helpful to students to address lecture notes. This method proposes a novel encoder-decoder deep model including two deep model networks which are convolutional and recurrent. The encoder part which consists of two convolutional layers followed by three recurrent layers of type bidirectional long short term memory. The decoder part which consists of one recurrent layer of type long short term memory. And also using attention mechanism layer. The proposed method training using standard CNN/Daily Mail dataset that achieved 92.90% accuracy. Index Terms— Text Summarization, Deep Learning, Convolutional Neural Network, Long Short Term Memory, Convolutional Recurrent Neural Network.

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Section: Related Workmentioning

confidence: 99%

Convolutional Recurrent Neural Networks for Text Lecture Summarization

2022

IJCCCE

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“…Due to its sequence-to-sequence nature, summarizing video, audio, pictures, and text is a difficult task [3], [4]. Deep learning methods have recently proven to be useful in a variety of domains, including image classification, summarization, machine translation, discourse identification, and text-to-speech production [5]- [8].…”

Section: Introductionmentioning

confidence: 99%

Multimodal video abstraction into a static document using deep learning

Abdulsahib

Abdulmunim

2023

IJECE

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<p><span lang="EN-US">Abstraction is a strategy that gives the essential points of a document in a short period of time. The video abstraction approach proposed in this research is based on multi-modal video data, which comprises both audio and visual data. Segmenting the input video into scenes and obtaining a textual and visual summary for each scene are the major video abstraction procedures to summarize the video events into a static document. To recognize the shot and scene boundary from a video sequence, a hybrid features method was employed, which improves detection shot performance by selecting strong and flexible features. The most informative keyframes from each scene are then incorporated into the visual summary. A hybrid deep learning model was used for abstractive text summarization. The BBC archive provided the testing videos, which comprised BBC Learning English and BBC News. In addition, a news summary dataset was used to train a deep model. The performance of the proposed approaches was assessed using metrics like Rouge for textual summary, which achieved a 40.49% accuracy rate. While precision, recall, and F-score used for visual summary have achieved (94.9%) accuracy, which performed better than the other methods, according to the findings of the experiments.</span></p>

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“…Several studies using Long Short-Term Memory (LSTM) to summarize documents have been conducted, including either extractive [15], [16] or abstractive summarization [17]- [19], which have proven the performance of LSTM in text summarization. In the text summarization of court decision documents, several methods such as LSA [20], and the merging of several methods such as LSA, LUHN, LEXRANK, and SUMBASIC [21], were employed.…”

Section: Introductionmentioning

confidence: 99%

Text Summarization on Verdicts of Industrial Relations Disputes Using the Cross-Latent Semantic Analysis and Long Short-Term Memory

Wicaksono,

Hakim,

Hayatin

et al. 2023

JOIV : Int. J. Inform. Visualization

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The information presented in the documents regarding industrial relations disputes constitutes four legal disputes. However, too much information leads to difficulty for readers to find essential points highlighted in industrial relations dispute documents. This research aims to summarize automated documents of court decisions over industrial relations disputes with permanent legal force. This research involved 35 documents of court decisions obtained from Indonesia’s official Supreme Court website and employed an extractive summarization approach to summarize the documents by utilizing Cross Latent Semantic Analysis (CLSA) and Long Short-Term Memory (LSTM) methods. The two methods are compared to obtain the best results CLSA was employed to analyze the connection between phrases, requiring the ordering of related words before they were converted into a complete summary. Then, the use of LSTM is combined with the Attention module to decoder and encoder the information entered so that it becomes a form that can be understood by the system and provides a variety of splitting of documents to be trained and tested to see the highest performance that the system can generate. The research has found out that the CLSA method gave a precision of 79.1%, recall score of 39.7%, and ROUGE-1 score of 50.9%, and the use of LSTM was able to improve the performance of the CLSA method with the results obtained 93.6%, recall score of 94.5 %, and ROUGE-1 score of 93.9% on the variation of splitting 95% training and 5% testing.

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Abstractive Text Summarization on Google Search Results

Cited by 6 publications

References 5 publications

Convolutional Recurrent Neural Networks for Text Lecture Summarization

Convolutional Recurrent Neural Networks for Text Lecture Summarization

Multimodal video abstraction into a static document using deep learning

Text Summarization on Verdicts of Industrial Relations Disputes Using the Cross-Latent Semantic Analysis and Long Short-Term Memory

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