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            <name>Title</name>
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                <text>Conference Papers</text>
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    <name>Conference Paper</name>
    <description>Faculty Publications- Conference Papers</description>
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          <name>Title</name>
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              <text>An Abstractive Text Summarization Using Decoder Attention with Pointer Network</text>
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          <name>Subject</name>
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              <text>Abstractive text summarization; Decoder Attention; Deep learning; Pointer network; Recall Oriented Understudy for Gisting Evaluation</text>
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              <text>Nowadays, large amounts of unstructured data are currently trending on social media and the Web. Text summarising is the process of extracting pertinent information in a concise manner without altering the content's core meaning. Summarising text by hand requires a lot of time, money, and effort. Although deep learning algorithms are commonly applied in abstractive text summarization, further research is clearly needed to fully understand their conjunction with semantic-based or structure-based approaches. The resume dataset is taken for this research work, which is gathered from Kaggle and the dataset includes 1,735 Resumes. This paper presents a unique framework based on the combination of semantic data transformations and deep learning approaches for improving abstractive text summarization. In an attempt to tackle the problem of unregistered words, a solution called Decoder Attention with Pointer Network (DA-PN) has been introduced. This method incorporates the use of a coverage mechanism to prevent word repetition in the generated text summaries. DA-PN is utilized for protecting the spread of increasing errors in generated text summaries. The performance of the proposed method is estimated using the evaluation indicator Recall Oriented Understudy for Gisting Evaluation (ROUGE) and attains an average of 26.28 which is comparatively higher than existing methods.  2023 IEEE.</text>
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          <name>Creator</name>
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              <text>Manohar M.; Sunil G.; Al-Attabi K.; Mohammed I.H.; Buvaneswari P.R.</text>
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          <name>Source</name>
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              <text>IEEE 1st International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics, AIKIIE 2023</text>
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          <name>Publisher</name>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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          <name>Date</name>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/AIKIIE60097.2023.10389942" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/AIKIIE60097.2023.10389942&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187305551&amp;amp;doi=10.1109%2FAIKIIE60097.2023.10389942&amp;amp;partnerID=40&amp;amp;md5=dbb63b50c4a4990d881040d53af48631" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85187305551&amp;amp;doi=10.1109%2fAIKIIE60097.2023.10389942&amp;amp;partnerID=40&amp;amp;md5=dbb63b50c4a4990d881040d53af48631&lt;/a&gt;</text>
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          <name>Rights</name>
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835031646-9</text>
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          <name>Format</name>
          <description>The file format, physical medium, or dimensions of the resource</description>
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              <text>Online</text>
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          <name>Language</name>
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              <text>English</text>
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          <name>Type</name>
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              <text>Conference paper</text>
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              <text>Manohar M., CHRIST(Deemed to Be University), Department of Computer Science and Engineering, Bangalore, India; Sunil G., School of Computer Science &amp;amp; Artificial Intelligence, Sr University, Warangal, India; Al-Attabi K., The Islamic University, Najaf, Iraq; Mohammed I.H., Al Rafidain University College, Department of Computer Communications Engineering, Bagdad, Iraq; Buvaneswari P.R., School of Electrical and Electronic Engineering, Vit Bhopal University, Sehhore, India</text>
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