A Systematic Literature Review on Text Generation Using Deep Neural Network Models
In recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie scriptwriting, and poetry composition, to n...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9771452/ |
| _version_ | 1818551437783728128 |
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| author | Noureen Fatima Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Abdullah Soomro |
| author_facet | Noureen Fatima Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Abdullah Soomro |
| author_sort | Noureen Fatima |
| collection | DOAJ |
| description | In recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie scriptwriting, and poetry composition, to name a few. The application of text generation in various fields has resulted in a lot of interest from the scientific community in this area. To the best of our knowledge, there is a lack of extensive review and an up-to-date body of knowledge of text generation deep learning models. Therefore, this survey aims to bring together all the relevant work in a systematic mapping study highlighting key contributions from various researchers over the years, focusing on the past, present, and future trends. In this work, we have identified 90 primary studies from 2015 to 2021 employing the PRISMA framework. We also identified research gaps that are further needed to be explored by the research community. In the end, we provide some future directions for researchers and guidelines for practitioners based on the findings of this review. |
| first_indexed | 2024-12-12T08:59:53Z |
| format | Article |
| id | doaj.art-52c6b540da8f4261a64a789d0ff38e6f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-12T08:59:53Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-52c6b540da8f4261a64a789d0ff38e6f2022-12-22T00:29:51ZengIEEEIEEE Access2169-35362022-01-0110534905350310.1109/ACCESS.2022.31741089771452A Systematic Literature Review on Text Generation Using Deep Neural Network ModelsNoureen Fatima0https://orcid.org/0000-0001-7423-9346Ali Shariq Imran1https://orcid.org/0000-0002-2416-2878Zenun Kastrati2https://orcid.org/0000-0002-0199-2377Sher Muhammad Daudpota3https://orcid.org/0000-0001-6684-751XAbdullah Soomro4Department of Computer Science, Sukkur IBA University, Sukkur, PakistanDepartment of Computer Science, Norwegian University of Science and Technology (NTNU), Gjøvik, NorwayDepartment of Informatics, Linnaeus University, Växjö, SwedenDepartment of Computer Science, Sukkur IBA University, Sukkur, PakistanDepartment of Computer Science, Sukkur IBA University, Sukkur, PakistanIn recent years, significant progress has been made in text generation. The latest text generation models are revolutionizing the domain by generating human-like text. It has gained wide popularity recently in many domains like news, social networks, movie scriptwriting, and poetry composition, to name a few. The application of text generation in various fields has resulted in a lot of interest from the scientific community in this area. To the best of our knowledge, there is a lack of extensive review and an up-to-date body of knowledge of text generation deep learning models. Therefore, this survey aims to bring together all the relevant work in a systematic mapping study highlighting key contributions from various researchers over the years, focusing on the past, present, and future trends. In this work, we have identified 90 primary studies from 2015 to 2021 employing the PRISMA framework. We also identified research gaps that are further needed to be explored by the research community. In the end, we provide some future directions for researchers and guidelines for practitioners based on the findings of this review.https://ieeexplore.ieee.org/document/9771452/Systematic literature reviewdeep learningtext generation surveynatural langauge processingquality metricsneural network |
| spellingShingle | Noureen Fatima Ali Shariq Imran Zenun Kastrati Sher Muhammad Daudpota Abdullah Soomro A Systematic Literature Review on Text Generation Using Deep Neural Network Models IEEE Access Systematic literature review deep learning text generation survey natural langauge processing quality metrics neural network |
| title | A Systematic Literature Review on Text Generation Using Deep Neural Network Models |
| title_full | A Systematic Literature Review on Text Generation Using Deep Neural Network Models |
| title_fullStr | A Systematic Literature Review on Text Generation Using Deep Neural Network Models |
| title_full_unstemmed | A Systematic Literature Review on Text Generation Using Deep Neural Network Models |
| title_short | A Systematic Literature Review on Text Generation Using Deep Neural Network Models |
| title_sort | systematic literature review on text generation using deep neural network models |
| topic | Systematic literature review deep learning text generation survey natural langauge processing quality metrics neural network |
| url | https://ieeexplore.ieee.org/document/9771452/ |
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