Summarize Smart: Domain-Specific Transformers for Legal and Financial Texts
DOI:
https://doi.org/10.48047/da4f5k58Keywords:
Natural Language Processing (NLP), Transformer Models, Legal Summarization, Financial Document Analysis, Hybrid Summarization, PEGASUS, T5, Named Entity Recognition (NER), ROUGE, BLEU, BERTScore.Abstract
Legal and financial documents are often lengthy, dense, and filled with specialized language, posing a challenge for efficient comprehension and decision-making. Manual summarization is time-consuming, error-prone, and inconsistent, emphasizing the need for automated systems that can deliver concise, accurate, and context-aware summaries. This paper presents a domain-adapted Natural Language
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