SlideShare a Scribd company logo
Impulse Technologies
                                      Beacons U to World of technology
        044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in
            A Context based Word Indexing Model for Document
                             Summarization
   Abstract
          Existing models for document summarization mostly use the similarity
   between sentences in the document to extract most salient sentences. The
   documents as well as the sentences are indexed using traditional term indexing
   measures, which do not take the context into consideration. Therefore, the sentence
   similarity values remain independent of the context. In this paper, we propose a
   context sensitive document indexing model based on the Bernoulli model of
   randomness. The Bernoulli model of randomness has been used to find the
   probability of the co-occurrences of two terms in a large corpora. A new approach
   using the lexical association between terms to give a context sensitive weight to the
   document terms has been proposed. The resulting indexing weights are used to
   compute the sentence similarity matrix. The proposed sentence similarity measure
   has been used with the baseline graph-based ranking models for sentence
   extraction. Experiments have been conducted over the benchmark DUC datasets
   and it has been shown that the proposed Bernoulli based sentence similarity model
   provides consistent improvements over the baseline IntraLink and UniformLink
   methods




  Your Own Ideas or Any project from any company can be Implemented
at Better price (All Projects can be done in Java or DotNet whichever the student wants)
                                                                                          1

More Related Content

What's hot

An efficient concept based mining model for enhancing text clustering(synopsis)
An efficient concept based mining model for enhancing text clustering(synopsis)An efficient concept based mining model for enhancing text clustering(synopsis)
An efficient concept based mining model for enhancing text clustering(synopsis)
Mumbai Academisc
 
Text Mining: (Asynchronous Sequences)
Text Mining: (Asynchronous Sequences)Text Mining: (Asynchronous Sequences)
Text Mining: (Asynchronous Sequences)
IJERA Editor
 
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
CSCJournals
 
Sentence similarity-based-text-summarization-using-clusters
Sentence similarity-based-text-summarization-using-clustersSentence similarity-based-text-summarization-using-clusters
Sentence similarity-based-text-summarization-using-clusters
MOHDSAIFWAJID1
 
Taxonomy extraction from automotive natural language requirements using unsup...
Taxonomy extraction from automotive natural language requirements using unsup...Taxonomy extraction from automotive natural language requirements using unsup...
Taxonomy extraction from automotive natural language requirements using unsup...
ijnlc
 
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
Order out of Chaos: Construction of Knowledge Models from PDF TextbooksOrder out of Chaos: Construction of Knowledge Models from PDF Textbooks
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
Isaac Alpizar-Chacon
 
Report
ReportReport
Report
butest
 
Integrating Textbooks with Smart Interactive Content for Learning Programming
Integrating Textbooks with Smart Interactive Content for Learning ProgrammingIntegrating Textbooks with Smart Interactive Content for Learning Programming
Integrating Textbooks with Smart Interactive Content for Learning Programming
Isaac Alpizar-Chacon
 
H04564550
H04564550H04564550
H04564550
IOSR-JEN
 
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONSSEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
IJDKP
 
Ijetcas14 624
Ijetcas14 624Ijetcas14 624
Ijetcas14 624
Iasir Journals
 
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORKMULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
Nexgen Technology
 
Contextual Definition Generation
Contextual Definition GenerationContextual Definition Generation
Contextual Definition Generation
Sergey Sosnovsky
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER) International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
ijceronline
 
Natural Language Processing Through Different Classes of Machine Learning
Natural Language Processing Through Different Classes of Machine LearningNatural Language Processing Through Different Classes of Machine Learning
Natural Language Processing Through Different Classes of Machine Learning
csandit
 
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
A review on Exploiting experts’ knowledge for structure learning of bayesian ...A review on Exploiting experts’ knowledge for structure learning of bayesian ...
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
Reza Sadeghi
 
Feature selection, optimization and clustering strategies of text documents
Feature selection, optimization and clustering strategies of text documentsFeature selection, optimization and clustering strategies of text documents
Feature selection, optimization and clustering strategies of text documents
IJECEIAES
 
Ay3313861388
Ay3313861388Ay3313861388
Ay3313861388
IJMER
 

What's hot (18)

An efficient concept based mining model for enhancing text clustering(synopsis)
An efficient concept based mining model for enhancing text clustering(synopsis)An efficient concept based mining model for enhancing text clustering(synopsis)
An efficient concept based mining model for enhancing text clustering(synopsis)
 
Text Mining: (Asynchronous Sequences)
Text Mining: (Asynchronous Sequences)Text Mining: (Asynchronous Sequences)
Text Mining: (Asynchronous Sequences)
 
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
An Efficient Semantic Relation Extraction Method For Arabic Texts Based On Si...
 
Sentence similarity-based-text-summarization-using-clusters
Sentence similarity-based-text-summarization-using-clustersSentence similarity-based-text-summarization-using-clusters
Sentence similarity-based-text-summarization-using-clusters
 
Taxonomy extraction from automotive natural language requirements using unsup...
Taxonomy extraction from automotive natural language requirements using unsup...Taxonomy extraction from automotive natural language requirements using unsup...
Taxonomy extraction from automotive natural language requirements using unsup...
 
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
Order out of Chaos: Construction of Knowledge Models from PDF TextbooksOrder out of Chaos: Construction of Knowledge Models from PDF Textbooks
Order out of Chaos: Construction of Knowledge Models from PDF Textbooks
 
Report
ReportReport
Report
 
Integrating Textbooks with Smart Interactive Content for Learning Programming
Integrating Textbooks with Smart Interactive Content for Learning ProgrammingIntegrating Textbooks with Smart Interactive Content for Learning Programming
Integrating Textbooks with Smart Interactive Content for Learning Programming
 
H04564550
H04564550H04564550
H04564550
 
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONSSEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
SEMANTICS GRAPH MINING FOR TOPIC DISCOVERY AND WORD ASSOCIATIONS
 
Ijetcas14 624
Ijetcas14 624Ijetcas14 624
Ijetcas14 624
 
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORKMULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
MULTILABEL CLASSIFICATION VIA CO-EVOLUTIONARY MULTILABEL HYPERNETWORK
 
Contextual Definition Generation
Contextual Definition GenerationContextual Definition Generation
Contextual Definition Generation
 
International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER) International Journal of Computational Engineering Research(IJCER)
International Journal of Computational Engineering Research(IJCER)
 
Natural Language Processing Through Different Classes of Machine Learning
Natural Language Processing Through Different Classes of Machine LearningNatural Language Processing Through Different Classes of Machine Learning
Natural Language Processing Through Different Classes of Machine Learning
 
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
A review on Exploiting experts’ knowledge for structure learning of bayesian ...A review on Exploiting experts’ knowledge for structure learning of bayesian ...
A review on Exploiting experts’ knowledge for structure learning of bayesian ...
 
Feature selection, optimization and clustering strategies of text documents
Feature selection, optimization and clustering strategies of text documentsFeature selection, optimization and clustering strategies of text documents
Feature selection, optimization and clustering strategies of text documents
 
Ay3313861388
Ay3313861388Ay3313861388
Ay3313861388
 

Similar to 7

AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
ijaia
 
An efficient approach for semantically enhanced document clustering by using ...
An efficient approach for semantically enhanced document clustering by using ...An efficient approach for semantically enhanced document clustering by using ...
An efficient approach for semantically enhanced document clustering by using ...
ijaia
 
Improving Text Categorization with Semantic Knowledge in Wikipedia
Improving Text Categorization with Semantic Knowledge in WikipediaImproving Text Categorization with Semantic Knowledge in Wikipedia
Improving Text Categorization with Semantic Knowledge in Wikipedia
chjshan
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESTHE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESTHE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
kevig
 
A comparative analysis of particle swarm optimization and k means algorithm f...
A comparative analysis of particle swarm optimization and k means algorithm f...A comparative analysis of particle swarm optimization and k means algorithm f...
A comparative analysis of particle swarm optimization and k means algorithm f...
ijnlc
 
Correlation Preserving Indexing Based Text Clustering
Correlation Preserving Indexing Based Text ClusteringCorrelation Preserving Indexing Based Text Clustering
Correlation Preserving Indexing Based Text Clustering
IOSR Journals
 
Research on ontology based information retrieval techniques
Research on ontology based information retrieval techniquesResearch on ontology based information retrieval techniques
Research on ontology based information retrieval techniques
Kausar Mukadam
 
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
IJDKP
 
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
ijdms
 
A survey on phrase structure learning methods for text classification
A survey on phrase structure learning methods for text classificationA survey on phrase structure learning methods for text classification
A survey on phrase structure learning methods for text classification
ijnlc
 
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGLARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
kevig
 
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGLARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
kevig
 
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET Journal
 
Co-Clustering For Cross-Domain Text Classification
Co-Clustering For Cross-Domain Text ClassificationCo-Clustering For Cross-Domain Text Classification
Co-Clustering For Cross-Domain Text Classification
paperpublications3
 
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
cscpconf
 
Document Classification Using KNN with Fuzzy Bags of Word Representation
Document Classification Using KNN with Fuzzy Bags of Word RepresentationDocument Classification Using KNN with Fuzzy Bags of Word Representation
Document Classification Using KNN with Fuzzy Bags of Word Representation
suthi
 
F017243241
F017243241F017243241
F017243241
IOSR Journals
 
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
iosrjce
 
A Novel Approach for Keyword extraction in learning objects using text mining
A Novel Approach for Keyword extraction in learning objects using text miningA Novel Approach for Keyword extraction in learning objects using text mining
A Novel Approach for Keyword extraction in learning objects using text mining
IJSRD
 

Similar to 7 (20)

AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
AN EFFICIENT APPROACH FOR SEMANTICALLYENHANCED DOCUMENT CLUSTERING BY USING W...
 
An efficient approach for semantically enhanced document clustering by using ...
An efficient approach for semantically enhanced document clustering by using ...An efficient approach for semantically enhanced document clustering by using ...
An efficient approach for semantically enhanced document clustering by using ...
 
Improving Text Categorization with Semantic Knowledge in Wikipedia
Improving Text Categorization with Semantic Knowledge in WikipediaImproving Text Categorization with Semantic Knowledge in Wikipedia
Improving Text Categorization with Semantic Knowledge in Wikipedia
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESTHE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
 
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESTHE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIES
 
A comparative analysis of particle swarm optimization and k means algorithm f...
A comparative analysis of particle swarm optimization and k means algorithm f...A comparative analysis of particle swarm optimization and k means algorithm f...
A comparative analysis of particle swarm optimization and k means algorithm f...
 
Correlation Preserving Indexing Based Text Clustering
Correlation Preserving Indexing Based Text ClusteringCorrelation Preserving Indexing Based Text Clustering
Correlation Preserving Indexing Based Text Clustering
 
Research on ontology based information retrieval techniques
Research on ontology based information retrieval techniquesResearch on ontology based information retrieval techniques
Research on ontology based information retrieval techniques
 
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATIONONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
ONTOLOGY INTEGRATION APPROACHES AND ITS IMPACT ON TEXT CATEGORIZATION
 
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
O NTOLOGY B ASED D OCUMENT C LUSTERING U SING M AP R EDUCE
 
A survey on phrase structure learning methods for text classification
A survey on phrase structure learning methods for text classificationA survey on phrase structure learning methods for text classification
A survey on phrase structure learning methods for text classification
 
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGLARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
 
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGLARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDING
 
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET- Short-Text Semantic Similarity using Glove Word Embedding
IRJET- Short-Text Semantic Similarity using Glove Word Embedding
 
Co-Clustering For Cross-Domain Text Classification
Co-Clustering For Cross-Domain Text ClassificationCo-Clustering For Cross-Domain Text Classification
Co-Clustering For Cross-Domain Text Classification
 
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
DOCUMENT SUMMARIZATION IN KANNADA USING KEYWORD EXTRACTION
 
Document Classification Using KNN with Fuzzy Bags of Word Representation
Document Classification Using KNN with Fuzzy Bags of Word RepresentationDocument Classification Using KNN with Fuzzy Bags of Word Representation
Document Classification Using KNN with Fuzzy Bags of Word Representation
 
F017243241
F017243241F017243241
F017243241
 
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
An Enhanced Suffix Tree Approach to Measure Semantic Similarity between Multi...
 
A Novel Approach for Keyword extraction in learning objects using text mining
A Novel Approach for Keyword extraction in learning objects using text miningA Novel Approach for Keyword extraction in learning objects using text mining
A Novel Approach for Keyword extraction in learning objects using text mining
 

More from Technology_solution

18
1818
17
1717
16
1616
15
1515
25
2525
24
2424
23
2323
22
2222
21
2121
20
2020
19
1919
18
1818
17
1717
16
1616
15
1515
14
1414
13
1313
12
1212
11
1111
10
1010

More from Technology_solution (20)

18
1818
18
 
17
1717
17
 
16
1616
16
 
15
1515
15
 
25
2525
25
 
24
2424
24
 
23
2323
23
 
22
2222
22
 
21
2121
21
 
20
2020
20
 
19
1919
19
 
18
1818
18
 
17
1717
17
 
16
1616
16
 
15
1515
15
 
14
1414
14
 
13
1313
13
 
12
1212
12
 
11
1111
11
 
10
1010
10
 

Recently uploaded

How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
Wahiba Chair Training & Consulting
 
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Diana Rendina
 
How to deliver Powerpoint Presentations.pptx
How to deliver Powerpoint  Presentations.pptxHow to deliver Powerpoint  Presentations.pptx
How to deliver Powerpoint Presentations.pptx
HajraNaeem15
 
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdfWalmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
TechSoup
 
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptxC1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
mulvey2
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
adhitya5119
 
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
iammrhaywood
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
Himanshu Rai
 
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityNatural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Akanksha trivedi rama nursing college kanpur.
 
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
National Information Standards Organization (NISO)
 
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold MethodHow to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
Celine George
 
Main Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docxMain Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docx
adhitya5119
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
Celine George
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
Jyoti Chand
 
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdfANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
Priyankaranawat4
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
Katrina Pritchard
 
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movieFilm vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
Nicholas Montgomery
 
PIMS Job Advertisement 2024.pdf Islamabad
PIMS Job Advertisement 2024.pdf IslamabadPIMS Job Advertisement 2024.pdf Islamabad
PIMS Job Advertisement 2024.pdf Islamabad
AyyanKhan40
 
The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
Israel Genealogy Research Association
 
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
สมใจ จันสุกสี
 

Recently uploaded (20)

How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience How to Create a More Engaging and Human Online Learning Experience
How to Create a More Engaging and Human Online Learning Experience
 
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
Reimagining Your Library Space: How to Increase the Vibes in Your Library No ...
 
How to deliver Powerpoint Presentations.pptx
How to deliver Powerpoint  Presentations.pptxHow to deliver Powerpoint  Presentations.pptx
How to deliver Powerpoint Presentations.pptx
 
Walmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdfWalmart Business+ and Spark Good for Nonprofits.pdf
Walmart Business+ and Spark Good for Nonprofits.pdf
 
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptxC1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
C1 Rubenstein AP HuG xxxxxxxxxxxxxx.pptx
 
Advanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docxAdvanced Java[Extra Concepts, Not Difficult].docx
Advanced Java[Extra Concepts, Not Difficult].docx
 
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptxNEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
NEWSPAPERS - QUESTION 1 - REVISION POWERPOINT.pptx
 
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem studentsRHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
RHEOLOGY Physical pharmaceutics-II notes for B.pharm 4th sem students
 
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama UniversityNatural birth techniques - Mrs.Akanksha Trivedi Rama University
Natural birth techniques - Mrs.Akanksha Trivedi Rama University
 
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
Pollock and Snow "DEIA in the Scholarly Landscape, Session One: Setting Expec...
 
How to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold MethodHow to Build a Module in Odoo 17 Using the Scaffold Method
How to Build a Module in Odoo 17 Using the Scaffold Method
 
Main Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docxMain Java[All of the Base Concepts}.docx
Main Java[All of the Base Concepts}.docx
 
How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17How to Make a Field Mandatory in Odoo 17
How to Make a Field Mandatory in Odoo 17
 
Wound healing PPT
Wound healing PPTWound healing PPT
Wound healing PPT
 
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdfANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
ANATOMY AND BIOMECHANICS OF HIP JOINT.pdf
 
BBR 2024 Summer Sessions Interview Training
BBR  2024 Summer Sessions Interview TrainingBBR  2024 Summer Sessions Interview Training
BBR 2024 Summer Sessions Interview Training
 
Film vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movieFilm vocab for eal 3 students: Australia the movie
Film vocab for eal 3 students: Australia the movie
 
PIMS Job Advertisement 2024.pdf Islamabad
PIMS Job Advertisement 2024.pdf IslamabadPIMS Job Advertisement 2024.pdf Islamabad
PIMS Job Advertisement 2024.pdf Islamabad
 
The Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collectionThe Diamonds of 2023-2024 in the IGRA collection
The Diamonds of 2023-2024 in the IGRA collection
 
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
คำศัพท์ คำพื้นฐานการอ่าน ภาษาอังกฤษ ระดับชั้น ม.1
 

7

  • 1. Impulse Technologies Beacons U to World of technology 044-42133143, 98401 03301,9841091117 ieeeprojects@yahoo.com www.impulse.net.in A Context based Word Indexing Model for Document Summarization Abstract Existing models for document summarization mostly use the similarity between sentences in the document to extract most salient sentences. The documents as well as the sentences are indexed using traditional term indexing measures, which do not take the context into consideration. Therefore, the sentence similarity values remain independent of the context. In this paper, we propose a context sensitive document indexing model based on the Bernoulli model of randomness. The Bernoulli model of randomness has been used to find the probability of the co-occurrences of two terms in a large corpora. A new approach using the lexical association between terms to give a context sensitive weight to the document terms has been proposed. The resulting indexing weights are used to compute the sentence similarity matrix. The proposed sentence similarity measure has been used with the baseline graph-based ranking models for sentence extraction. Experiments have been conducted over the benchmark DUC datasets and it has been shown that the proposed Bernoulli based sentence similarity model provides consistent improvements over the baseline IntraLink and UniformLink methods Your Own Ideas or Any project from any company can be Implemented at Better price (All Projects can be done in Java or DotNet whichever the student wants) 1