A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization
Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of me...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2021-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9328413/ |
| _version_ | 1818562788179574784 |
|---|---|
| author | Ayesha Ayub Syed Ford Lumban Gaol Tokuro Matsuo |
| author_facet | Ayesha Ayub Syed Ford Lumban Gaol Tokuro Matsuo |
| author_sort | Ayesha Ayub Syed |
| collection | DOAJ |
| description | Dealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task. |
| first_indexed | 2024-12-14T01:08:19Z |
| format | Article |
| id | doaj.art-a936d782270e475c9307de25214a603e |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T01:08:19Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-a936d782270e475c9307de25214a603e2022-12-21T23:22:52ZengIEEEIEEE Access2169-35362021-01-019132481326510.1109/ACCESS.2021.30527839328413A Survey of the State-of-the-Art Models in Neural Abstractive Text SummarizationAyesha Ayub Syed0https://orcid.org/0000-0002-3113-8980Ford Lumban Gaol1https://orcid.org/0000-0002-5116-5708Tokuro Matsuo2Department of Doctor of Computer Science, Bina Nusantara University, Jakarta, IndonesiaDepartment of Doctor of Computer Science, Bina Nusantara University, Jakarta, IndonesiaGraduate School of Industrial Technology, Advanced Institute of Industrial Technology, Tokyo, JapanDealing with vast amounts of textual data requires the use of efficient systems. Automatic summarization systems are capable of addressing this issue. Therefore, it becomes highly essential to work on the design of existing automatic summarization systems and innovate them to make them capable of meeting the demands of continuously increasing data, based on user needs. This study tends to survey the scientific literature to obtain information and knowledge about the recent research in automatic text summarization specifically abstractive summarization based on neural networks. A review of various neural networks based abstractive summarization models have been presented. The proposed conceptual framework includes five key elements identified as encoder-decoder architecture, mechanisms, training strategies and optimization algorithms, dataset, and evaluation metric. A description of these elements is also included in this article. The purpose of this research is to provide an overall understanding and familiarity with the elements of recent neural networks based abstractive text summarization models with an up-to-date review as well as to render an awareness of the challenges and issues with these systems. Analysis has been performed qualitatively with the help of a concept matrix indicating common trends in the design of recent neural abstractive summarization systems. Models employing a transformer-based encoder-decoder architecture are found to be the new state-of-the-art. Based on the knowledge acquired from the survey, this article suggests the use of pre-trained language models in complement with neural network architecture for abstractive summarization task.https://ieeexplore.ieee.org/document/9328413/Abstractive text summarizationencoderdecodertrainingoptimizationevaluation |
| spellingShingle | Ayesha Ayub Syed Ford Lumban Gaol Tokuro Matsuo A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization IEEE Access Abstractive text summarization encoder decoder training optimization evaluation |
| title | A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization |
| title_full | A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization |
| title_fullStr | A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization |
| title_full_unstemmed | A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization |
| title_short | A Survey of the State-of-the-Art Models in Neural Abstractive Text Summarization |
| title_sort | survey of the state of the art models in neural abstractive text summarization |
| topic | Abstractive text summarization encoder decoder training optimization evaluation |
| url | https://ieeexplore.ieee.org/document/9328413/ |
| work_keys_str_mv | AT ayeshaayubsyed asurveyofthestateoftheartmodelsinneuralabstractivetextsummarization AT fordlumbangaol asurveyofthestateoftheartmodelsinneuralabstractivetextsummarization AT tokuromatsuo asurveyofthestateoftheartmodelsinneuralabstractivetextsummarization AT ayeshaayubsyed surveyofthestateoftheartmodelsinneuralabstractivetextsummarization AT fordlumbangaol surveyofthestateoftheartmodelsinneuralabstractivetextsummarization AT tokuromatsuo surveyofthestateoftheartmodelsinneuralabstractivetextsummarization |