Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Book Chapter Faculty Publications- Book Chapter Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Sridhar, M.; Irfan, Mohammad; Kathirmani, Sukumar Title A name given to the resource Study of State-of-the-Art Performance Metrics in NLP: Specifically for Text Summarization in the Medical Domain Using the SumPubMed Dataset Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Generative AI: Disruptive Technologies for Innovative Applications;pp.91-106 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1002/9781394302932.ch5" target="_blank" rel="noreferrer noopener">https://doi.org/10.1002/9781394302932.ch5</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105012674558?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105012674558?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Sridhar M., NSB Academy, Business School, Bangalore, India; Irfan M., Christ University, Bangalore, India; Kathirmani S., Quelit Innovations Pvt Ltd, Chennai, India Description An account of the resource Text summarization is becoming very important given the number of documents produced each year across domains. In this paper we explore the various traditional metrics for text summarization, such as ROUGE, BLEU, METEOR, etc., and look at improving the performance of the existing metric by taking the stateof-the-art untrained metric SUPERT, and clubbing it with a readability score and a penalty for long summaries. The SUMPUBMED dataset was used for this research and a BERT extractive summarizer was used for generating the summaries. It was found that using a readability score with an unsupervised metric such as SUPERT helped in assessing the quality of the summary more accurately than earlier metrics. We compared the metrics such as SUPERT scores and BERT scores with and without involving the human annotated summaries in the SUMPUBMED dataset and found that untrained metrics perform better than when involving a reference annotated summary. 2025 Scrivener Publishing LLC. Subject The topic of the resource BERT scores; Metrics; NLP; SUPERT; Text summarization Publisher An entity responsible for making the resource available wiley Relation A related resource ISBN: 978-139430293-2; 978-139430290-1; Language A language of the resource English Type The nature or genre of the resource Book chapter Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online