Text Summarization using Textrank, Lexrank and Bart model
Sherin Mariam Jijo, Disha Panchal, Jalpa Ardeshana, Urvashi M. Chaudhari · 2024
In natural language processing, text summarization is crucial for applications like information retrieval, content generation, resource optimization, and legal and academic research. It creates a concise version of the original text without omitting crucial information, thereby facilitating the efficient understanding and processing of large data volumes. Researchers are increasingly focusing on developing more effective summarization techniques. This paper reviews existing text summarization methods, including the Textrank algorithm, fuzzy logic, Latent Semantic Analysis, and deep learning techniques. We have implemented Textrank, LexRank, and the BART model on the news summary dataset. For evaluation, we utilized ROUGE1, ROUGE2, and ROUGEL metrics, considering precision, recall, and F-measure.