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              <text>Domain-Driven Summarization: Models for Diverse Content Realms</text>
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              <text>Automatic text summarization; Computational resource optimization; Domain-specific enhancements; Domain-specific optimizations; Natural language processing domain-agnostic summarization; Summarization techniques</text>
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              <text>In todays information-rich landscape, automatic text summarization systems are pivotal in condensing extensive textual content into concise and informative summaries. The current study ventures into domain-agnostic summarization, delving into advanced models spanning various domains, such as business, entertainment, sports, politics, and technology. The study aims to uncover domain-specific enhancements, assess resource efficiency, and explore the boundaries of applicability. This study covers nine cutting-edge models, including Google Pegasus-Large, Facebook BART-Base, SSHLEIFER DistilBART-CNN-6-6, Facebook BART-Large, T5-Large, T5-Base, Facebook BART-Large-CNN, Facebook BART-Large-Xsum, and SSHLEIFER DistilBART-Xsum-12-1. Each model undergoes rigorous evaluation, revealing its efficacy within various domains. Google Pegasus-Large emerges as a standout choice for cross-domain summarization, while Facebook BART-Base demonstrates remarkable stability. Models like SSHLEIFER DistilBART-CNN-6-6, T5 variants, and others contribute to the evolving landscape of summarization. This study endeavors to establish a robust foundation for enhancing the efficiency and effectiveness of summarization techniques within various domains, thereby contributing valuable insights to the broader literature on text summarization.  The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.</text>
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              <text>Prince D.; Jacob L.; Reddy K.B.</text>
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              <text>Lecture Notes in Networks and Systems, Vol-922 LNNS, pp. 291-298.</text>
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              <text>Springer Science and Business Media Deutschland GmbH</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1007/978-981-97-0975-5_26" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1007/978-981-97-0975-5_26&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197228679&amp;amp;doi=10.1007%2F978-981-97-0975-5_26&amp;amp;partnerID=40&amp;amp;md5=7fee53c54aaed3f7333aff3a8a1178fc" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197228679&amp;amp;doi=10.1007%2f978-981-97-0975-5_26&amp;amp;partnerID=40&amp;amp;md5=7fee53c54aaed3f7333aff3a8a1178fc&lt;/a&gt;</text>
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              <text>ISSN: 23673370; ISBN: 978-981970974-8</text>
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              <text>Prince D., Christ (Deemed to be University), Bangalore, India; Jacob L., Christ (Deemed to be University), Bangalore, India; Reddy K.B., Christ (Deemed to be University), Bangalore, India</text>
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