Authors : Utsha Sarker; Lalit Vaishnav; Archy Biswas; Ashish Raj; Saurabh

Volume/Issue : Volume 10 - 2025, Issue 11 - November

Google Scholar : https://tinyurl.com/5xp6nu7e

Scribd : https://tinyurl.com/5xrs58bu

DOI : https://doi.org/10.38124/ijisrt/25nov797

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Abstract : The main reason for the high effectiveness of text summarization is due to the success of LLMs for this task and across different domains. This work aims at understanding how LLMs are used to summarize domains and make it more accurate and efficient. We discuss how current models perform with regard to specialized information, with focus on the financial and medical domains. The work suggests that an approach using Vertex AI, a generative machine learning platform in the cloud, can be used to assess pre-trained summarization models for different tasks. Most of the research presented in the paper also reveals the efficacy of Vertex AI for text summarization with high accuracy and efficiency. We demonstrate the applicability of the platform for summarizing transcripts and dialogues, generating bullet points, titles and to- do lists. Also, the research show that Vertex AI is reliable in terms of cost since it can be used by businesses and individual researchers.

Keywords : LLM, Summarization, Domain-Specific, Vertex AI, Generative Models, ML, NLP, Finance, Healthcare, Evaluation, ROUGE, Cloud-Based, AI, Data Science, Text Mining.

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