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Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/26087Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Onah, DFO | - |
| dc.contributor.author | Pang, ELL | - |
| dc.contributor.author | El-Haj, M | - |
| dc.coverage.spatial | Osaka, Japan | - |
| dc.date.accessioned | 2023-03-08T13:55:11Z | - |
| dc.date.available | 2023-03-08T13:55:11Z | - |
| dc.date.issued | 2022-12-17 | - |
| dc.identifier.citation | Onah, D.F.O., Pang, E.L.L. and El-Haj, M. (2022) 'A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling', 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17-20 December 2022, pp. 1 - 11. doi: 10.1109/BigData55660.2022.10020259. | en_US |
| dc.identifier.isbn | 978-1-6654-8045-1 (ebk) | - |
| dc.identifier.issn | 978-1-6654-8046-8 (PoD) | - |
| dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26087 | - |
| dc.format.extent | 1 - 11 | - |
| dc.format.medium | Print-Electronic | - |
| dc.language.iso | en_US | en_US |
| dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
| dc.rights | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
| dc.subject | summarization | en_US |
| dc.subject | extractive | en_US |
| dc.subject | abstractive | en_US |
| dc.subject | latent Dirichlet | en_US |
| dc.subject | allocation | en_US |
| dc.subject | topic modelling | en_US |
| dc.subject | visualisation | en_US |
| dc.subject | ROUGE | en_US |
| dc.title | A Data-driven Latent Semantic Analysis for Automatic Text Summarization using LDA Topic Modelling | en_US |
| dc.type | Conference Paper | en_US |
| dc.identifier.doi | https://doi.org/10.1109/BigData55660.2022.10020259 | - |
| dc.relation.isPartOf | 2022 IEEE International Conference on Big Data (Big Data) | - |
| pubs.finish-date | 2022-12-20 | - |
| pubs.start-date | 2022-12-17 | - |
| dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
| Appears in Collections: | The Brunel Collection | |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| FullText.pdf | Copyright © 2022 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works by sending a request to pubs-permissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | 2.54 MB | Adobe PDF | View/Open |
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