Extractive text summarization technique using fuzzy C-means clustering algorithm
| bracu.type.group | Research Publications | |
| datacite.rights | Metadata Only | |
| dc.contributor.author | Gani, Shahul | |
| dc.contributor.author | Uddin, Jia | |
| dc.contributor.author | Mobin, Md. Iftekharul | |
| dc.contributor.department | Department of Computer Science and Engineering, | |
| dc.date.accessioned | 2026-08-27T11:14:23Z | |
| dc.date.available | 2026-08-27T11:14:23Z | |
| dc.date.issued | 2019-07-01 | |
| dc.description.abstract | Text summarization process has become one of the significant research areas for years owing to cope up with the astounding increase of virtual textual materials. Text summarization is the process to keep the relevant important information of the original text in a shorter version with the main ideas of the original text. There are two main classifications of text summarization process Extractive and Abstractive text summarization, Extractive smnmarization processes by using most important fragments of existing words, phrases or sentences from the original document, A sentence based mode) using Fuzzy C-Means clustering has been proposed in this research. Six best key features including a new feature 'Sentence Highlighter Feature' have been introduced for the sentence scoring. Performance of the proposed FCM mode) is evaluated by ROUGE, which has been gauged with the precision, recall and f-measure. The result shows that this FCM model extractive techniques with a less outline repetition and profundity of data. | |
| dc.description.version | Published | |
| dc.format.extent | 4 Pages | |
| dc.identifier.citation | S. Gani, J. Uddin and M. I. Mobin, "Extractive Text Summarization Technique Using Fuzzy C-Means Clustering Algorithm," 2019 International Conference on Computer, Communication, Chemical, Materials and Electronic Engineering (IC4ME2), Rajshahi, Bangladesh, 2019, pp. 1-6, doi: 10.1109/IC4ME247184.2019.9036642. | |
| dc.identifier.doi | 10.1109/IC4ME247184.2019.9036642 | |
| dc.identifier.issn | 9781728130606 | |
| dc.identifier.other | 2-s2.0-85082988373 | |
| dc.identifier.uri | https://hdl.handle.net/10361/29575 | |
| dc.language.iso | en_US | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | |
| dc.relation.hasversion | 10.1109/IC4ME247184.2019.9036642 | |
| dc.relation.ispartof | 5th International Conference on Computer Communication Chemical Materials and Electronic Engineering Ic4me2 2019 | |
| dc.relation.ispartofseries | 5th International Conference on Computer Communication Chemical Materials and Electronic Engineering Ic4me2 2019 | |
| dc.relation.uri | https://ieeexplore.ieee.org/document/9036642 | |
| dc.subject | Clustering algorithms | |
| dc.subject | Mathematical model | |
| dc.subject | Feature extraction | |
| dc.subject | Silicon | |
| dc.subject | Partitioning algorithms | |
| dc.subject | Chemicals | |
| dc.subject | Data mining | |
| dc.subject | Sentence extraction | |
| dc.subject | Clustering | |
| dc.subject | Summarization | |
| dc.subject.lcsh | Automatic abstracting. | |
| dc.subject.lcsh | Natural language processing (Computer science). | |
| dc.title | Extractive text summarization technique using fuzzy C-means clustering algorithm | |
| dc.type | Conference Proceeding | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.affiliation.name | BRAC University | |
| person.identifier.scopus-author-id | 57216271427 | |
| person.identifier.scopus-author-id | 54994936900 | |
| person.identifier.scopus-author-id | 55545997800 |