Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features
Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score e...
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Gespeichert in:
| Autor*in: |
Sharan, Aditi [verfasserIn] Yadav, Chandra Shekhar [verfasserIn] |
|---|
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E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2015 |
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| Umfang: |
1 Online-Ressource |
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| Übergeordnetes Werk: |
Enthalten in: International journal of information retrieval research - Hershey, Pa : IGI Global, 2011, 5(2015), 4, Seite 46-70 |
|---|---|
| Übergeordnetes Werk: |
volume:5 ; year:2015 ; number:4 ; pages:46-70 |
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| DOI / URN: |
10.4018/IJIRR.2015100104 |
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10.4018/IJIRR.2015100104 doi (DE-627)NLEJ251811867 (VZGNL)10.4018/IJIRR.2015100104 DE-627 ger DE-627 rakwb eng Sharan, Aditi verfasserin aut Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score Hybrid Model Sentiment Analysis Single Document Summarization Summarization Yadav, Chandra Shekhar verfasserin aut Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 5(2015), 4, Seite 46-70 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:5 year:2015 number:4 pages:46-70 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2015 4 46-70 |
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10.4018/IJIRR.2015100104 doi (DE-627)NLEJ251811867 (VZGNL)10.4018/IJIRR.2015100104 DE-627 ger DE-627 rakwb eng Sharan, Aditi verfasserin aut Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score Hybrid Model Sentiment Analysis Single Document Summarization Summarization Yadav, Chandra Shekhar verfasserin aut Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 5(2015), 4, Seite 46-70 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:5 year:2015 number:4 pages:46-70 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2015 4 46-70 |
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10.4018/IJIRR.2015100104 doi (DE-627)NLEJ251811867 (VZGNL)10.4018/IJIRR.2015100104 DE-627 ger DE-627 rakwb eng Sharan, Aditi verfasserin aut Hybrid Approach for Single Text Document Summarization Using Statistical and Sentiment Features 2015 1 Online-Ressource Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score Hybrid Model Sentiment Analysis Single Document Summarization Summarization Yadav, Chandra Shekhar verfasserin aut Enthalten in International journal of information retrieval research Hershey, Pa : IGI Global, 2011 5(2015), 4, Seite 46-70 Online-Ressource (DE-627)NLEJ244419159 (DE-600)2703390-9 2155-6385 nnns volume:5 year:2015 number:4 pages:46-70 http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104 X:IGIG Verlag Deutschlandweit zugänglich http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.2015100104&buylink=true Abstract ZDB-1-GIS GBV_NL_ARTICLE AR 5 2015 4 46-70 |
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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score |
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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score |
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Summarization is a way to represent same information in concise way with equal sense. This can be categorized in two type Abstractive and Extractive type. Our work is focused around Extractive summarization. A generic approach to extractive summarization is to consider sentence as an entity, score each sentence based on some indicative features to ascertain the quality of sentence for inclusion in summary. Sort the sentences on the score and consider top n sentences for summarization. Mostly statistical features have been used for scoring the sentences. A hybrid model for a single text document summarization is being proposed. This hybrid model is an extraction based approach, which is combination of Statistical and semantic technique. The hybrid model depends on the linear combination of statistical measures: sentence position, TF-IDF, Aggregate similarity, centroid, and semantic measure. The idea to include sentiment analysis for salient sentence extraction is derived from the concept that emotion plays an important role in communication to effectively convey any message hence, it can play a vital role in text document summarization. For comparison, five system summaries have been generated: Proposed Work, MEAD system, Microsoft system, OPINOSIS system, and Human generated summary, and evaluation is done using ROUGE score |
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10.4018/IJIRR.2015100104 |
| up_date |
2024-07-06T11:40:26.887Z |
| _version_ |
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7.3981 |