Abstractive Text Summarization with LSTM using Beam Search Inference Phase Decoder and Attention Mechanism

Patel, Ruchir M.; Goswami, Ankur

doi:10.1109/iccisc52257.2021.9484880

Cited by 2 publications

(2 citation statements)

References 8 publications

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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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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

View full text Add to dashboard Cite

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“…We use an approach proposed by Petal et al [36] to set up the encoderdecoder (training and inference) and to achieve the best possible results, we also used:…”

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confidence: 99%

Visual Analytics Using Machine Learning for Transparency Requirements

Fadloun,

Bennamane,

Meshoul

et al. 2023

Mathematics

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Problem solving applications require users to exercise caution in their data usage practices. Prior to installing these applications, users are encouraged to read and comprehend the terms of service, which address important aspects such as data privacy, processes, and policies (referred to as information elements). However, these terms are often lengthy and complex, making it challenging for users to fully grasp their content. Additionally, existing transparency analytics tools typically rely on the manual extraction of information elements, resulting in a time-consuming process. To address these challenges, this paper proposes a novel approach that combines information visualization and machine learning analyses to automate the retrieval of information elements. The methodology involves the creation and labeling of a dataset derived from multiple software terms of use. Machine learning models, including naïve Bayes, BART, and LSTM, are utilized for the classification of information elements and text summarization. Furthermore, the proposed approach is integrated into our existing visualization tool TranspVis to enable the automatic detection and display of software information elements. The system is thoroughly evaluated using a database-connected tool, incorporating various metrics and expert opinions. The results of our study demonstrate the promising potential of our approach, serving as an initial step in this field. Our solution not only addresses the challenge of extracting information elements from complex terms of service but also provides a foundation for future research in this area.

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Abstractive Text Summarization with LSTM using Beam Search Inference Phase Decoder and Attention Mechanism

Cited by 2 publications

References 8 publications

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

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

Visual Analytics Using Machine Learning for Transparency Requirements

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