Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/39426
Title: Role of classification in automated extractive text summarization with linear regression and summary refinement
Researcher: Esther hannah M
Guide(s): Saswati mukherjee
Keywords: Natural Language Processing Community
Single document extractive text summarization
Upload Date: 22-Apr-2015
University: Anna University
Completed Date: 01/10/2014
Abstract: Text mining is a relatively new field emerging in many disciplines It is becoming more popular as technology advances and the need for efficient text analysis is required The aim of text mining itself is not to provide strict rules by analysing the full data set but is used to predict with some certainty while only analysing a small portion of the text The increasing availability of online information has necessitated intensive research in the area of automatic text summarization within the Natural Language Processing Community Extensive use of internet is perhaps one of the main reasons why automatic text summarization draws substantial interest newlineText Summarization is the task of producing a summary as a text that is produced from one or more texts that convey important information in the original text and that is no longer than half of the original text and usually significantly less than that Depending on its form a summary can be classified as extractive or abstractive the former being an actual representation of paragraphs sentences or phrases from the original document and the latter being a concise summary of the central subject matter of a document Single document extractive text summarization is focused in this thesis and unique features that will help in selecting summary sentences are also identified and extracted newline newline
Pagination: xx, 156p.
URI: http://hdl.handle.net/10603/39426
Appears in Departments:Faculty of Information and Communication Engineering

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01_title.pdfAttached File21.72 kBAdobe PDFView/Open
02_certificate.pdf358.5 kBAdobe PDFView/Open
03_abstract.pdf9.31 kBAdobe PDFView/Open
04_acknowledgement.pdf8.94 kBAdobe PDFView/Open
05_content.pdf30.62 kBAdobe PDFView/Open
06_chapter1.pdf77 kBAdobe PDFView/Open
07_chapter2.pdf108.38 kBAdobe PDFView/Open
08_chapter3.pdf153.35 kBAdobe PDFView/Open
09_chapter4.pdf291.54 kBAdobe PDFView/Open
10_chapter5.pdf619.71 kBAdobe PDFView/Open
11_chapter6.pdf336.24 kBAdobe PDFView/Open
12_chapter7.pdf8.81 kBAdobe PDFView/Open
13_reference.pdf720.47 kBAdobe PDFView/Open
14_publication.pdf29.66 kBAdobe PDFView/Open


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