TIIS (한국인터넷정보학회)
Current Result Document : 17 / 17
| 한글제목(Korean Title) |
An Efficient Machine Learning-based Text Summarization in the Malayalam Language |
| 영문제목(English Title) |
An Efficient Machine Learning-based Text Summarization in the Malayalam Language |
| 저자(Author) |
Rosna P Haroon
Abdul Gafur M
Barakkath Nisha U
|
| 원문수록처(Citation) |
VOL 16 NO. 05 PP. 1778 ~ 1799 (2022. 05) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Automatic text summarization is a procedure that packs enormous content into a more limited book that incorporates significant data. Malayalam is one of the toughest languages utilized in certain areas of India, most normally in Kerala and in Lakshadweep. Natural language processing in the Malayalam language is relatively low due to the complexity of the language as well as the scarcity of available resources. In this paper, a way is proposed to deal with the text summarization process in Malayalam documents by training a model based on the Support Vector Machine classification algorithm. Different features of the text are taken into account for training the machine so that the system can output the most important data from the input text. The classifier can classify the most important, important, average, and least significant sentences into separate classes and based on this, the machine will be able to create a summary of the input document. The user can select a compression ratio so that the system will output that much fraction of the summary. The model performance is measured by using different genres of Malayalam documents as well as documents from the same domain. The model is evaluated by considering content evaluation measures precision, recall, F score, and relative utility. Obtained precision and recall value shows that the model is trustable and found to be more relevant compared to the other summarizers.
|
| 키워드(Keyword) |
SVM
Text Mining
Sentence Extraction
Summary Generation
Supervised Machine Learning
Malayalam Text Summarization
|
| 파일첨부 |
PDF 다운로드
|
