Transliteration based Generative Pre-trained Transformer 2 Model for Tamil Text Summarization

Dhivyaa, C.R.; Nithya, K.; Janani, T.; Kumar, K. Sathis; Prashanth, N.

doi:10.1109/iccci54379.2022.9740991

Cited by 10 publications

(3 citation statements)

References 22 publications

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“…They used two datasets, and using their proposed method, they achieved 78.36% precision, 79.5% recall, and 78.96% F-measure as, 79.57. For Tamil text summarization, (Dhivyaa et al, 2022) proposed transliteration and transformer-based summarization of Tamil text. For fine-tuning, they used three pre-trained models i.e.…”

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 target sentence's transliteration is then produced by the decoder. For machine translation, well-known neural models include LSTM [6], Imran [7] conducted recent research utilizing advanced transformer models such as BERT and RoBERTa. Their study implemented a Seq2seq model, which employed a bi-directional decoder.…”

Section: E Neural-based Approachmentioning

confidence: 99%

TAMZHI: Shorthand Romanized Tamil to Tamil Reverse Transliteration Using Novel Hybrid Approach

Herath,

Sumanathilaka

2024

Int J on Adv. in ICT for Emerging Countries

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Transliteration from Tamil to the Roman script holds a crucial place in the realms of effective communication, educational accessibility, and the seamless integration of digital technology. However, this process encounters a significant challenge due to the disparity in the number of vowels between the Tamil script, which encompasses a rich set of 12 vowels, and the Roman script, which is limited to just 5. This incongruity poses a substantial impediment when attempting ad-hoc transliteration of Tamil into the Roman script, especially when vowels are omitted. This paper aims to make a significant academic contribution by conducting an extensive literature review of recent developments in Romanized Tamil to Tamil transliteration, with a particular focus on addressing the absence of vowels. The review involves a meticulous examination of a wide array of methodologies proposed in recent years, ranging from rule-based systems to context-based strategies and machine learning-based approaches. In response to the challenges inherent in Tamil to Roman transliteration, this research work introduces a novel and innovative solution. This solution incorporates a Reverse Transliteration module, which leverages N-gram analysis and a rule-based model. The utilization of a trained trie structure is a key component of this approach, enabling word suggestions that effectively resolve ambiguities during the transliteration process. Remarkably, the proposed solution outperforms existing character-level transliteration methods, achieving an impressive character-level accuracy rate of 0.93. The practical implications of this research are substantial, particularly concerning the fulfilment of the linguistic and transliteration needs of native Tamil speakers within the digital platform, where such accuracy is of utmost importance.

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“…In the natural language processing field, several sub-fields are very closely related to information retrieval, such as the automatic text summarization sub-field (Dhivyaa et al, 2022) (Farahani et al, 2021), text summarization is a way to produce a digest consisting of information in a document consisting of subject objects and predicate (Reddy & Guha, 2023) (Lubis, Prayudani, et al, 2022) (Lubis & Nasution, 2023). The purpose of text summarization is to display information from longer documents into documents with text that is more concise and can be easily understood (Lalitha et al, 2023) (Alambo et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

Lubis,

Safitri,

Irvan

et al. 2023

JAETS

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In the natural language processing field, there are several sub-fields that are very closely related to information retrieval, such as the automatic text summarization sub-field. obtained from the convolutional T5 and Seq2Seq models in summarizing text on hugging faces found features that can affect text summary such as upper- and lower-case letters which have an impact on changing the understanding of the text of the document. This study uses a combination of parameters such as layer dimensions, learning rate, batch size, and the use of Dropout to avoid model overfitting. The results can be seen by evaluating metrics using ROUGE. This study produces a value of ROUGE-1 on 4 documents that are tested which produces an average of 0.8 which is the optimal value, for ROUGE-2 on 4 documents that are tested which results in an average of 0.83 which is an optimal value while ROUGE-L on 4 documents conducted tests that produce an average of 0.8 which is the optimal value for the summary model.

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Transliteration based Generative Pre-trained Transformer 2 Model for Tamil Text Summarization

Cited by 10 publications

References 22 publications

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

Hindi Abstractive Text Summarization using Transliteration with Pre-trained Model

TAMZHI: Shorthand Romanized Tamil to Tamil Reverse Transliteration Using Novel Hybrid Approach

Improving Text Summarization Quality by Combining T5-Based Models and Convolutional Seq2Seq Models

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