Attention based Abstractive Summarization of Malayalam Document

Nambiar, Sindhya K; S, David Peter; Idicula, Sumam Mary

doi:10.1016/j.procs.2021.05.088

Cited by 7 publications

(2 citation statements)

References 8 publications

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“…Using their proposed method, they were able to generate a clear and concise summary. (Nambiar et al, 2021) proposed abstractive text summarization of Malayalam text. They used the Seq2Seq model with an attention mechanism for summarizing Malayalam text.…”

Section: Summarization Of Indian Languagesmentioning

confidence: 99%

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Shashi Shekhar, Rashmi Gupta

2024

jes

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Automatic text summarization is a subarea of natural language processing that generates a summary of the text by keeping its key points. The research work done on summarizing low-resourced language text is very limited. In India, the Hindi language is being spoken by central and north Indian people and only a few research works have been done on abstractive summarization of Hindi language. Having matras in Hindi makes it difficult to tokenize so it is difficult to summarize Hindi text using abstractive text summarization. In the proposed method, abstractive Hindi text summarization is done using transliteration and fine-tuning. In this work, the model is trained to generate both summaries and headlines. ROUGE-score and BERT-score have been utilized to check summary quality. A new semantic similarity score-based performance measure is also proposed to measure semantic similarity between reference summaries and predicted summaries. Using the proposed method, we have achieved the highest 55.16 ROUGE score, 0.80 BERT score, and 0.98 similarity score. Along with these performance measures, human evaluation of predicted summaries is also done and it is found that summaries and headlines were generated at a human-acceptable level.

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Section: Summarization Of Indian Languagesmentioning

confidence: 99%

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Shashi Shekhar, Rashmi Gupta

2024

jes

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“…The peculiar complexity of the Malayalam language, such as the frequent usage of linking verbs or copulas in sentences and the lack of predicate agreements between subject and verbs about person, gender, and number, are highlighted in the study [9]. The classic S2S attention model is trained using data created by translating Malayalam versions of freely downloadable BBC news corpus.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Text Summarization Approach Using Neural Networks and Metaheuristic Algorithms

Zeyad,

Biradar

2023

IJSSE

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The rapid growth of text data on the Internet requires effective automatic text summarization techniques. This study proposes a hybrid text summarization approach that combines a Multi-hidden Recurrent Neural Network and a mayfly-harmony search algorithm. The neural network generates a feature vector for each sentence. The mayflyharmony search algorithm then optimizes the feature weights to extract the most relevant sentences for the summary. This manuscript capacity provides essential information and expertise that can be effectively summarised using Efficient Abstractive Text Summarising (EATS) techniques. This project aimed to extract informative summaries from various articles by utilizing regularly utilized handcrafted elements from literature. A Multi-hidden Recurrent Neural Network (MRNN) was used to generate a feature vector, and a new feature assortment strategy called Mayfly-Harmony Search (MHS) was applied for feature extraction. The number of sentences, word frequency, title similarity, term frequency-inverse sentence frequency, sentence location, sentence length, sentencesentence similarity, sentence phrases, proper nouns, n-gram co-occurrence, and document length were the features used. By taking diverse Mayfly Algorithm explanations found from other expanses of the search space and processing them with Harmony Search, the suggested hybrid of the Mayfly Algorithm and Harmony Search was employed to produce superior results.

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Text summarization using residual-based temporal attention convolutional neural network

Rajan,

Jose

2023

Int. j. inf. tecnol.

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Attention based Abstractive Summarization of Malayalam Document

Cited by 7 publications

References 8 publications

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

A Hybrid Text Summarization Approach Using Neural Networks and Metaheuristic Algorithms

Text summarization using residual-based temporal attention convolutional neural network

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