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In this chapter, we examine about some similar applications for “E-Note Mate”, and technologies that have used in their artifact.
Summary Scanner is an android application (figure 1) developed by summery scanner, which provides scanning and convert images quickly to digital copy. In here user need to take image or have to select image on the mobile gallery. Then System will automatically convert it to digital document. App will allow more functionality (figure 2) such as summarize, translate, automatic question generation, Speed reading, share and export as a PDF. However, translate is not working properly. There are many languages to select for the translation but it is able to translate around 2 languages.
But this application is not mainly developed for the student. This system missed some vital functionality, however predicted artifact is basically targeting to student and added many more functionalities compare to this application.
Yogan jaya Kumar et al (2016) according to this article examine the necessity of text summarization, Automatic text Summarization, the methods that have been used and some areas of text summarization. In addition, this article considers the sentence extraction, domain specific summarization as well as multi document summarization and provides relevant logical example and basic concepts. In extract summarization is identify and extract important document text and organize as a summery. As well as here describe three subsection of extract summarization such as features base approaches, Frequency base approaches and machine learning base approaches. If consider about domain specific summarization this article reviews medical document summarization, news document and email and they used special and unique characteristics to summarize. Finally examine multi document summarization they review some related works, using some methods such as cluster Based, Graph based method and Discourse Based method.
Wencan Luo and Diane Litman(2015) This paper proposed to automatically summarize student responses to reflection prompts and automatically novel summarizing algorithm different from the other methods. When linguistic unit of student inputs single word to multiple tenses, this summarizer created extend phases rather than sentences, Furthermore, the phase summarization algorithm, they assume that the concepts mentioned by more student should get more attention from the instructor. Causes this article introduce the notation of student coverage, determine as the number of student who semantically mention a phase in a summary. The suggested algorithm has three partition which are candidate phrase extraction this is using syntax parser from the Senna toolkit, phrase clustering this use clustering paradigm with semantic distance metric. To clustering K-Medoids algorithm is fit well for tire requirements.
Hyoungil Jeong et al (2010)At present most of the people use smart phone to read news article, magazine etc. However it is difficult to read huge article in small hand held devices like mobile phone. In this article they propose, summarize is best way to come up with these problems. Because of that, this paper proposed system which aims to develop automatic keyword extraction, text summarization techniques and search engine. As well as system provides multiple news article summarization. It can be useful when searched articles will be multiple news articles. Furthermore apply to Korean and English news article summarization method. The proposed system can provide keywords, summary of single and multiple articles and search for the user giving details.
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