Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020.
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Brac University
2021
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| 在线阅读: | http://dspace.bracu.ac.bd/xmlui/handle/10361/14446 |
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10361-144462022-01-26T10:21:47Z Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm Haider, Mofiz Mojib Hossin, Md. Arman Mahi, Hasibur Rashid Arif, Hossain Department of Computer Science and Engineering, Brac University Text summarization Extractive Single Document NLP Gensim Word2Vec K-Means This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. Cataloged from PDF version of thesis. Includes bibliographical references (pages 28-29). The significance of text summarization in the Natural Language Processing (NLP) community has now expanded because of the staggering increase in virtual textual materials. Text summary is the process created from one or multiple texts which convey important insight in a little form of the main text. Multiple text summarization technique assists to pick indispensable points of the original texts reducing time and effort require reading the whole document. The question was approached from a different point of view, in a different domain by using different concepts. Extractive and abstractive are the two main methods of summing up text. Though extractive summary is primarily concerned with what summary content the frequency of words, phrases, and sentences from the original document should be used. This research proposes a sentence based clustering algorithm (K-Means) for a single document. For feature extraction, we have used Gensim word2vec which is intended to automatically extract semantic topics from documents in the most efficient way possible. Mofiz Mojib Haider Md. Arman Hossin Hasibur Rashid Mahi B. Computer Science 2021-05-29T15:48:36Z 2021-05-29T15:48:36Z 2020 2020-04 Thesis ID: 16301038 ID: 17301214 ID: 16301035 http://dspace.bracu.ac.bd/xmlui/handle/10361/14446 en_US 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. 29 pages application/pdf Brac University |
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Brac University |
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Institutional Repository |
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en_US |
| topic |
Text summarization Extractive Single Document NLP Gensim Word2Vec K-Means |
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Text summarization Extractive Single Document NLP Gensim Word2Vec K-Means Haider, Mofiz Mojib Hossin, Md. Arman Mahi, Hasibur Rashid Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2020. |
| author2 |
Arif, Hossain |
| author_facet |
Arif, Hossain Haider, Mofiz Mojib Hossin, Md. Arman Mahi, Hasibur Rashid |
| format |
Thesis |
| author |
Haider, Mofiz Mojib Hossin, Md. Arman Mahi, Hasibur Rashid |
| author_sort |
Haider, Mofiz Mojib |
| title |
Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| title_short |
Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| title_full |
Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| title_fullStr |
Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| title_full_unstemmed |
Automatic text summarization using Gensim Word2Vec and K-Means Clustering Algorithm |
| title_sort |
automatic text summarization using gensim word2vec and k-means clustering algorithm |
| publisher |
Brac University |
| publishDate |
2021 |
| url |
http://dspace.bracu.ac.bd/xmlui/handle/10361/14446 |
| work_keys_str_mv |
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