End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric
Urdu, being a common language in South Asia, has not received significant attention in terms of language processing compared to more advanced languages. In the field of Natural Language Processing (NLP), the task of text summarization holds great importance due to its ability to comprehend textual c...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10472483/ |
| _version_ | 1827308178585419776 |
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| author | Hassan Raza Waseem Shahzad |
| author_facet | Hassan Raza Waseem Shahzad |
| author_sort | Hassan Raza |
| collection | DOAJ |
| description | Urdu, being a common language in South Asia, has not received significant attention in terms of language processing compared to more advanced languages. In the field of Natural Language Processing (NLP), the task of text summarization holds great importance due to its ability to comprehend textual content and generate concise summaries. Text summarization can be either extractive or abstractive in nature. While considerable efforts have been made to advance extractive summarization techniques, the limitations associated with it have been extensively explored and explained in the paper. However, the domain of abstractive summarization for the Urdu language remains largely unexplored. The challenges and underlying factors that have impeded progress in this domain have also been addressed. This paper specifically focuses on abstractive summarization of the Urdu language using supervised learning. To accomplish this, a labeled dataset consisting of Urdu text and its abstractive summaries is required. A dataset of Urdu text and its corresponding abstractive summaries has been prepared for the purpose of supervised learning. Additionally, the paper presents the results of summary generation, measured in terms of a rough score. Transformer’s encoder-decoder network was employed to generate abstractive summaries in Urdu, yielding a ROUGE-1 score of 25.18 in Urdu text summarization. Moreover, a novel evaluation metric called the “disconnection rate” has been introduced as a context-aware evaluation metric to enhance the assessment of a summary, known as the Context Aware RoBERTa Score. |
| first_indexed | 2024-04-24T18:52:52Z |
| format | Article |
| id | doaj.art-736a577ad67b4fe58bf354331c507c90 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-24T18:52:52Z |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-736a577ad67b4fe58bf354331c507c902024-03-26T17:48:20ZengIEEEIEEE Access2169-35362024-01-0112403114032410.1109/ACCESS.2024.337746310472483End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation MetricHassan Raza0https://orcid.org/0009-0001-4857-4818Waseem Shahzad1https://orcid.org/0000-0002-9491-3761FAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, PakistanFAST School of Computing, National University of Computer and Emerging Sciences, Islamabad, PakistanUrdu, being a common language in South Asia, has not received significant attention in terms of language processing compared to more advanced languages. In the field of Natural Language Processing (NLP), the task of text summarization holds great importance due to its ability to comprehend textual content and generate concise summaries. Text summarization can be either extractive or abstractive in nature. While considerable efforts have been made to advance extractive summarization techniques, the limitations associated with it have been extensively explored and explained in the paper. However, the domain of abstractive summarization for the Urdu language remains largely unexplored. The challenges and underlying factors that have impeded progress in this domain have also been addressed. This paper specifically focuses on abstractive summarization of the Urdu language using supervised learning. To accomplish this, a labeled dataset consisting of Urdu text and its abstractive summaries is required. A dataset of Urdu text and its corresponding abstractive summaries has been prepared for the purpose of supervised learning. Additionally, the paper presents the results of summary generation, measured in terms of a rough score. Transformer’s encoder-decoder network was employed to generate abstractive summaries in Urdu, yielding a ROUGE-1 score of 25.18 in Urdu text summarization. Moreover, a novel evaluation metric called the “disconnection rate” has been introduced as a context-aware evaluation metric to enhance the assessment of a summary, known as the Context Aware RoBERTa Score.https://ieeexplore.ieee.org/document/10472483/Datasetsneural networksCA-RoBERTa scoretext summarization |
| spellingShingle | Hassan Raza Waseem Shahzad End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric IEEE Access Datasets neural networks CA-RoBERTa score text summarization |
| title | End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric |
| title_full | End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric |
| title_fullStr | End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric |
| title_full_unstemmed | End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric |
| title_short | End to End Urdu Abstractive Text Summarization With Dataset and Improvement in Evaluation Metric |
| title_sort | end to end urdu abstractive text summarization with dataset and improvement in evaluation metric |
| topic | Datasets neural networks CA-RoBERTa score text summarization |
| url | https://ieeexplore.ieee.org/document/10472483/ |
| work_keys_str_mv | AT hassanraza endtoendurduabstractivetextsummarizationwithdatasetandimprovementinevaluationmetric AT waseemshahzad endtoendurduabstractivetextsummarizationwithdatasetandimprovementinevaluationmetric |