A review of graph-based extractive text summarization models
The amount of text data is continuously increasing both at online and offline storage, that makes is difficult for people to read across and find the desired information within a possible available time. This necessitate the use of technique such as automatic text summarization. A text summary is th...
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| Main Authors: | , , , |
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| Format: | Article |
| Published: |
Springer Science and Business Media Deutschland GmbH
2021
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| Subjects: | |
| Online Access: | http://eprints.utm.my/id/eprint/100257/ http://dx.doi.org/10.1007/978-3-030-70713-2_41 |
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| Summary: | The amount of text data is continuously increasing both at online and offline storage, that makes is difficult for people to read across and find the desired information within a possible available time. This necessitate the use of technique such as automatic text summarization. A text summary is the briefer form of the original text, in which the principal document message is preserved. Many approaches and algorithms have been proposed for automatic text summarization including, supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a review of various graph-based automatic text summarization models. |
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