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TRANSCRIPT
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SCIENTIFIC DOCUMENT SUMMARIZATION
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ABSTRACT Aims at extracting main Ideas of a document in a short and readable paragraphs. Sentence extraction-based single document summarization. Content based document summarizing is done. Bernoulli model algorithm is used for content extraction. Finally summary is created in the text format.
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INTRODUCTION Document summarization
- Information retrieval task.- Gives overview of large document.
Readers may decide whether or not to read complete
document. Basically summarization is divided into two
- Extraction based summarization.
- Abstraction based summarization.
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Cont..... We focuses on extraction based single document
summarization. We emphasis on scientific paper summarization. Document uploaded can be a text document ,a word
document(.doc or .docx ) or a pdf. The document type is then covert into format.
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Cont..... Bernoulli model algorithm is used to calculate informative terms.
- TF(Term Frequency) is calculated.- Tagging are done.- Sentence Ranking is done.
Finally summary is created in the text format.
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BASIC BLOCK DIAGRAMUpload Document
Word Tokenization & Preprocessing
Sentence Extraction
Application of Bernolli Model
Algorithm
Sentence Ranking
Summary Creation
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PROJECT SPECIFICATION
Processor Intel Core 2 duo or above
Memory 4 GB DDR3 RAM
Display Any display that supports
1024x768 resolution
Hardware Specification
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Cont….
Operating System Windows 8/7,Linux
Web Server Apache Tomcat 7
Web Browser Google Chrome or Internet
Explorer
Database MySQL 5.3
Technology and Developing
Tool
Python
IDE Python IDLE
Software Specification
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DETAILS OF THE WORK User can login and upload the document. Document uploaded can be a text document ,a word
document(. doc or .docx )or a pdf. Identify the document type and covert into text file. From the uploaded document, first words are extracted
then sentences. Bernoulli model algorithm is used to calculate informative terms.
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Cont.... Steps included are : 1. Preprocessing and Word Tokenizing - Store the extracted words from the uploaded document to DB - Eliminate the stop words(in,it,or,of,etc) . 2. Sentence Extraction - Extract the sentence from the text content by using break iterator and store to DB.
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Cont....3. Application of Bernoulli model algorithm - Calculating how informative is each of the document terms. - TF is calculated. TF = No of words found Total no :of words in document - Penn Tagging (NN,NNS etc) and Modal Tagging (must, should etc) is done. - weight of the sentences is found.
X 100
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Cont....4.Sentence Ranking Steps involved are :- - select sentences which contains the word TF>Default value. - select the sentences which contains the modal tags. - retrieve the distinct sentences from these two sets.
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PROJECT CURRENT STATUS
Login ,signup & Upload pages have been created. Database connectivity and validation for each pages
have been done. Analyzed IEEE papers based on project. Analyzed the relevance of topic.
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EXPECTED OUTCOME
Summarize large document to short and readable paragraphs. Main sentences will be included in the output. Reader can save time using this application.
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Q & A