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DC Field | Value | Language |
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dc.contributor.author | Fiham, M.Z.M | - |
dc.date.accessioned | 2021-08-03T07:10:57Z | - |
dc.date.available | 2021-08-03T07:10:57Z | - |
dc.date.issued | 2021-08-03 | - |
dc.identifier.uri | http://dl.ucsc.cmb.ac.lk/jspui/handle/123456789/4386 | - |
dc.description.abstract | Information are the most valuable assets in the current world. In the past people kept the information as physically. With the vast improvement of the information, they tried to store the information in digitally. People are reluctant to read large information. So, people tried to read the summary to understand without reading all the information. Summarization was done by the human because of no other mechanism. Manually summarization was so time consuming and high costly. Human resources for summarization also not available because of high demand and time consuming. Which ever the large content there is only a main idea that’s included inside the content. Other information will be a descriptive information around the main idea. Motivation to find the main idea and show the summary to the readers. Automatic text summarization introduces to the following contextual problem. This research is intended to find the English language news to be summarized with the most suitable approach and features to give high level of accuracy to the information. Using single document summarization with extractive methodology and abstractive techniques will be used in this research for generate the news summary. By using above mentioned techniques achieved an English summary of the news article. With using abstractive technique leads the news article with simplified summary which can read and understand by any novice users. Above mentioned methodology will help to differentiate with the other researchers’ outcomes. | en_US |
dc.language.iso | en | en_US |
dc.title | English news summarization from online sources | en_US |
dc.type | Thesis | en_US |
Appears in Collections: | 2019 |
Files in This Item:
File | Description | Size | Format | |
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2016MCS030.pdf | 540.56 kB | Adobe PDF | View/Open |
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