Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey
With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a sig...
| 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/9994688/ |
| _version_ | 1797974416988045312 |
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| author | Divakar Yadav Rishabh Katna Arun Kumar Yadav Jorge Morato |
| author_facet | Divakar Yadav Rishabh Katna Arun Kumar Yadav Jorge Morato |
| author_sort | Divakar Yadav |
| collection | DOAJ |
| description | With the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data; hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field. |
| first_indexed | 2024-04-11T04:19:35Z |
| format | Article |
| id | doaj.art-818c3d43bf564c218b8006dc843d2205 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-04-11T04:19:35Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-818c3d43bf564c218b8006dc843d22052022-12-31T00:01:28ZengIEEEIEEE Access2169-35362022-01-011013398113400310.1109/ACCESS.2022.32310169994688Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art SurveyDivakar Yadav0https://orcid.org/0000-0001-6051-479XRishabh Katna1Arun Kumar Yadav2Jorge Morato3https://orcid.org/0000-0002-7530-9753School of Computer and Information Sciences (SOCIS), Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi, IndiaDepartment of Computer Science and Engineering, National Institute of Technology Hamirpur (NIT Hamirpur), Hamirpur, Himachal Pradesh, IndiaSchool of Computer and Information Sciences (SOCIS), Indira Gandhi National Open University (IGNOU), Maidan Garhi, New Delhi, IndiaDepartment of Computer Science, Universidad Carlos III de Madrid, Leganés, SpainWith the advent of the World Wide Web, there are numerous online platforms that generate huge amounts of textual material, including social networks, online blogs, magazines, etc. This textual content contains useful information that can be used to advance humanity. Text summarization has been a significant area of research in natural language processing (NLP). With the expansion of the internet, the amount of data in the world has exploded. Large volumes of data make locating the required and best information time-consuming. It is impractical to manually summarize petabytes of data; hence, computerized text summarization is rising in popularity. This study presents a comprehensive overview of the current status of text summarizing approaches, techniques, standard datasets, assessment criteria, and future research directions. The summarizing approaches are assessed based on several characteristics, including approach-based, document-number-based, Summarization domain-based, document-language-based, output summary nature, etc. This study concludes with a discussion of many obstacles and research opportunities linked to text summarizing research that may be relevant for future researchers in this field.https://ieeexplore.ieee.org/document/9994688/Abstractive summarizationcosine-similaritydeep learningextractive summarizationgraph-based algorithmneural networks |
| spellingShingle | Divakar Yadav Rishabh Katna Arun Kumar Yadav Jorge Morato Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey IEEE Access Abstractive summarization cosine-similarity deep learning extractive summarization graph-based algorithm neural networks |
| title | Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey |
| title_full | Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey |
| title_fullStr | Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey |
| title_full_unstemmed | Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey |
| title_short | Feature Based Automatic Text Summarization Methods: A Comprehensive State-of-the-Art Survey |
| title_sort | feature based automatic text summarization methods a comprehensive state of the art survey |
| topic | Abstractive summarization cosine-similarity deep learning extractive summarization graph-based algorithm neural networks |
| url | https://ieeexplore.ieee.org/document/9994688/ |
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