Browsing by Author "Sekhri Thamer Bedida Mohammed ."

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    AI Approach For Automatic Text Summarization
    (university of eloued جامعة الوادي, 2024-06-24) Sekhri Thamer Bedida Mohammed .
    æ Ag Ég ñëð , ú Í B@ ñ JË@ J jÊ K ÈAm . × ékðQ£ B@ è Yë ÈðA J K . A J ÓñK Aë ðA @ Õ æK ú æË@ éJ JË@ HA KAJ J . Ë@ áÓ éÊ KAêË@ éJ ÒºË@ èP@X@ ø Yj , JË éJ k . @Q j J B@ I . J ËA B@ ½Ë X ú ¯ AÖ ß . , é ®Ê J jÖ Ï @ J jÊ JË@ HAJ J ® K ­ º J AêÒJ J ® Kð éJ ËAm Ì '@ h . XAÒ JÊË éÊÓA éªk . @QÓ A J @PX ÉÒ . é JJ j . êË@ð , éK YK Qj . JË@ á j JË HAJ J ® JË@ è Yë ú ¯ èñ ®Ë@  A ® K á K . ©Òm . ' A J m × @ PA£@ h Q ® K . éJ ËAª ¯ HA KAJ K . é«ñÒm . × úÎ « IK Qk . @ ú æË@ H . PAj . JË@ Qê ¢ . J jÊ JË@ èXñk . ú ¯ á ®Ë@ øñ J Ö ß . © ¯YK AÜ Ø , éº AÒ JÓð è Qk . ñÓ HA jÊÓ YJ Ëñ K ú This thesis investigates the field of automatic text summarization, a crucial solution to the challenge of managing the vast amount of textual data generated daily. We explore various summarization techniques, including extractive, abstractive, and hybrid approaches. Our study includes a thorough review of existing models and their evaluation. We propose an enhanced framework that combines the strengths of these techniques to improve summarization quality. Experiments conducted on the CNN/DailyMail dataset demonstrate the effectiveness of our approach in generating concise and coherent summaries, advancing the state-of-the-art in text summarization ¯ A Jj . î E .