يجب تسجيل الدخول أولا
Minimum Redundancy And Maximum Relevance For Single And Multi Document Arabic Text Summarization
| المصدر: | مجلة جامعة الملك سعود - علوم الحاسب والمعلومات |
|---|---|
| الناشر: | جامعة الملك سعود |
| المؤلف الرئيسي: | Oufaida, Houda (Author) |
| مؤلفين آخرين: | Nouali, Omar (Co-Author) , Blache, Philippe (Co-Author) |
| المجلد/العدد: | مج26, ع4 |
| محكمة: | نعم |
| الدولة: |
السعودية |
| التاريخ الميلادي: |
2014
|
| الصفحات: | 450 - 461 |
| DOI: |
10.33948/0584-026-004-010 |
| ISSN: |
1319-1578 |
| رقم MD: | 973382 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | science |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Arabic Text Summarization | Sentence Extraction | Mrmr | Minimum Redundancy | Maximum Relevance
|
| رابط المحتوى: |
| المستخلص: |
Automatic text summarization aims to produce summaries for one or more texts using machine techniques. In this paper, we propose a novel statistical summarization system for Arabic texts. Our system uses a clustering algorithm and an adapted discriminant analysis method: mRMR (minimum redundancy and maximum relevance) to score terms. Through mRMR analysis, terms are ranked according to their discriminant and coverage power. Second, we propose a novel sentence extraction algorithm which selects sentences with top ranked terms and maximum diversity. Our system uses minimal language-dependant processing: sentence splitting, tokenization and root extraction. Experimental results on EASC and TAC 2011 Multi Lingual datasets showed that our proposed approach is competitive to the state of the art systems. |
|---|---|
| ISSN: |
1319-1578 |
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