Extractive text summarization using Fuzzy-c-means clustering
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.
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BRAC University
2019
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| 在线阅读: | http://hdl.handle.net/10361/11440 |
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10361-114402022-01-26T10:21:50Z Extractive text summarization using Fuzzy-c-means clustering Gani, Shafiul Uddin, Jia Department of Computer Science and Engineering, BRAC University Sentence extraction Clustering Summarization Text processing (Computer science) Document clustering. Cluster analysis--Computer programs. This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. Includes bibliographical references (pages 31-33). Cataloged from PDF version of thesis. Keeping track of the precise information from a large volume of text is an arduous task for human. Test summarization process has become one of the significant research areas for years owing to cope up with the astounding increase of virtual textual material. 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 for understanding innumerable volumes of information easily within a short period of time. There are two main classifications of text summarization process, Extractive and Abstractive text summarization. Extractive summarization processes by using most important fragments of exiting words, phrases or sentences from the original document. It largely depends on sentence-extraction techniques or sentence-based model. A sentence based model using Fuzzy C-Means clustering has been proposed this research. Six key features including a new feature have been added for the sentence scoring. Performance of the proposed FCM model is evaluated by ROUGE, which has been gauged with the precision, recall and f-measure.The result shows that this FCM model interprets extractive text summarization methods with a less summary redundancy and depth of information and also it shows more adhering and coherent than other previous approaches. Keywords: Sentence Extraction, Clustering, Summarization. Shafiul Gani B. Computer Science and Engineering 2019-02-19T06:56:35Z 2019-02-19T06:56:35Z 2018 2018-12 Thesis ID 14101213 http://hdl.handle.net/10361/11440 en BRAC University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 33 pages application/pdf BRAC University |
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Brac University |
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Institutional Repository |
| language |
English |
| topic |
Sentence extraction Clustering Summarization Text processing (Computer science) Document clustering. Cluster analysis--Computer programs. |
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Sentence extraction Clustering Summarization Text processing (Computer science) Document clustering. Cluster analysis--Computer programs. Gani, Shafiul Extractive text summarization using Fuzzy-c-means clustering |
| description |
This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018. |
| author2 |
Uddin, Jia |
| author_facet |
Uddin, Jia Gani, Shafiul |
| format |
Thesis |
| author |
Gani, Shafiul |
| author_sort |
Gani, Shafiul |
| title |
Extractive text summarization using Fuzzy-c-means clustering |
| title_short |
Extractive text summarization using Fuzzy-c-means clustering |
| title_full |
Extractive text summarization using Fuzzy-c-means clustering |
| title_fullStr |
Extractive text summarization using Fuzzy-c-means clustering |
| title_full_unstemmed |
Extractive text summarization using Fuzzy-c-means clustering |
| title_sort |
extractive text summarization using fuzzy-c-means clustering |
| publisher |
BRAC University |
| publishDate |
2019 |
| url |
http://hdl.handle.net/10361/11440 |
| work_keys_str_mv |
AT ganishafiul extractivetextsummarizationusingfuzzycmeansclustering |
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