Monolingual text summarization for I ndic Languages using LLM s (2024)

Abstract: We have analyzed the growth of advanced text summarization method leveraging LLM for Indic language. Text summarization involves transforming a longer text information into a more concise version, ensuring that the most prominent information and key meanings are maintained. Our goal is to produce concise and accurate summaries from longer texts, focusing on maintaining detailed information and coherence. We utilize NLP techniques for text cleaning, keyword extraction and summarization, along with performance evaluation metrics such as ROUGE score, BLEU score and BERT Score. The results demonstrate an incremental improvement in the quality of generated summaries, with a particular emphasis on enhancing informativeness while minimizing redundancy. This research work also highlights the importance of tuning parameters and leveraging advanced models for producing high quality summaries in diverse domains for Indic Language.

## introduction the process of generating longer textual content into the shorter version while preserving the prime information and its meaning defined as text summarization. effective summarization enhances readability and aids in quick understanding of the content @xcite ). text summarization is a critical tool in the digital age, allowing for the extraction of important information from large content of data @xcite . text summarization in low resource language which is the most challenging one in the field of nlp. in this paper, we focus on refine text summarization for english and indic tamil language by implementing our model using the gemini llm @xcite . the challenges which lies in ensuring that the generated summaries are both comprehensive and concise, covering all essential aspects of the original content while avoiding redundancy @xcite . we applied several natural language processing techniques, including text cleaning and keyword extraction using the rake algorithm, to ensure the quality of the input data. our model utilizes the map-reduce summarization chain, designed to retain important details in the output summary @xcite . we then evaluate the generated summaries against reference summaries using multiple performance metrics including rougescore, bleuscore and bertscore. to evaluate the model success in balancing coverage, precision and readability the performance metrics are used @xcite . by focusing on important sentences, we demonstrate the model's ability to generate insightful, well-rounded summaries @xcite .