Survey of Deep Learning Table-to-Text Generation
Text generation is a hot field in natural language processing. With the increasing capability of information collection, more and more structured data, such as tables, are collected. How to solve the problem of information overload, understand the table meaning and describe the table content is an i...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2022-11-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2204089.pdf |
| _version_ | 1798017617203560448 |
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| author | HU Kang, XI Xuefeng, CUI Zhiming, ZHOU Yueyao, QIU Yajin |
| author_facet | HU Kang, XI Xuefeng, CUI Zhiming, ZHOU Yueyao, QIU Yajin |
| author_sort | HU Kang, XI Xuefeng, CUI Zhiming, ZHOU Yueyao, QIU Yajin |
| collection | DOAJ |
| description | Text generation is a hot field in natural language processing. With the increasing capability of information collection, more and more structured data, such as tables, are collected. How to solve the problem of information overload, understand the table meaning and describe the table content is an important problem of artificial intelli-gence, so the task of table-to-text generation appears. Table-to-text generation refers to the language model input table data generated after the corresponding text description of the table. The text description generated by the model should express the information of the table smoothly and not deviate from the fact of the table. Firstly, this paper describes and defines the task background from table-to-text generation in detail, analyzes the main difficulties of the task, and introduces the main research methods. There are two major issues on table-to-text generation: what to describe and how to describe it. This paper summarizes the methods proposed by different researchers to solve these two problems, and summarizes the characteristics, advantages and disadvantages of the proposed models. The performance of these excellent models on the main dataset is compared and analyzed. At the same time, the models are classified according to the model type, and the horizontal comparative analysis is carried out. This paper also introduces the common evaluation methods in the field of table-to-text generation, and summaries the characte-ristics, advantages and disadvantages of different evaluation methods. Finally, this paper prospects the future development trend of table-to-text generation task. |
| first_indexed | 2024-04-11T16:10:14Z |
| format | Article |
| id | doaj.art-ece08133a577480a97b8ff26dcfb0545 |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-04-11T16:10:14Z |
| publishDate | 2022-11-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-ece08133a577480a97b8ff26dcfb05452022-12-22T04:14:43ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182022-11-0116112487250410.3778/j.issn.1673-9418.2204089Survey of Deep Learning Table-to-Text GenerationHU Kang, XI Xuefeng, CUI Zhiming, ZHOU Yueyao, QIU Yajin01. School of Electronic and Information Engineering, Suzhou University of Science and Technology, Suzhou, Jiangsu 215000, China;2. Suzhou Key Laboratory of Virtual Reality Intelligent Interaction and Application Technology, Suzhou, Jiangsu 215000, China;3. Suzhou Smart City Research Institute, Suzhou, Jiangsu 215000, ChinaText generation is a hot field in natural language processing. With the increasing capability of information collection, more and more structured data, such as tables, are collected. How to solve the problem of information overload, understand the table meaning and describe the table content is an important problem of artificial intelli-gence, so the task of table-to-text generation appears. Table-to-text generation refers to the language model input table data generated after the corresponding text description of the table. The text description generated by the model should express the information of the table smoothly and not deviate from the fact of the table. Firstly, this paper describes and defines the task background from table-to-text generation in detail, analyzes the main difficulties of the task, and introduces the main research methods. There are two major issues on table-to-text generation: what to describe and how to describe it. This paper summarizes the methods proposed by different researchers to solve these two problems, and summarizes the characteristics, advantages and disadvantages of the proposed models. The performance of these excellent models on the main dataset is compared and analyzed. At the same time, the models are classified according to the model type, and the horizontal comparative analysis is carried out. This paper also introduces the common evaluation methods in the field of table-to-text generation, and summaries the characte-ristics, advantages and disadvantages of different evaluation methods. Finally, this paper prospects the future development trend of table-to-text generation task.http://fcst.ceaj.org/fileup/1673-9418/PDF/2204089.pdf|natural language processing|text generation|structured data|table-to-text generation |
| spellingShingle | HU Kang, XI Xuefeng, CUI Zhiming, ZHOU Yueyao, QIU Yajin Survey of Deep Learning Table-to-Text Generation Jisuanji kexue yu tansuo |natural language processing|text generation|structured data|table-to-text generation |
| title | Survey of Deep Learning Table-to-Text Generation |
| title_full | Survey of Deep Learning Table-to-Text Generation |
| title_fullStr | Survey of Deep Learning Table-to-Text Generation |
| title_full_unstemmed | Survey of Deep Learning Table-to-Text Generation |
| title_short | Survey of Deep Learning Table-to-Text Generation |
| title_sort | survey of deep learning table to text generation |
| topic | |natural language processing|text generation|structured data|table-to-text generation |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2204089.pdf |
| work_keys_str_mv | AT hukangxixuefengcuizhimingzhouyueyaoqiuyajin surveyofdeeplearningtabletotextgeneration |