An impact of firefly multi-objective optimization algorithm in the process of text summarization for generation good summaries

Abstract

The amount of information available on the internet is increasing at a rapid pace. As a result, it is critical that we may quickly and readily get the information we want without having to go through lengthy documentation. Automatic text summarization (ATS) is a technique for producing concise overviews of documents while retaining the most significant information. Everyone wants to finish tasks in the smallest amount of time feasible in today's age of continuously developing technology. Optimization algorithms can be used to determine the most significant lines and subjects in the text and to decrease the quantity of text in the summary. This helps to ensure that the summary is brief and useful while retaining the most relevant information from the original text. In this research, we introduced effective automated text summarizing strategies for multi-document text summarization based on the firefly optimization algorithm. The proposed algorithm's performance was evaluated using text summarization benchmark datasets from the Document Understanding Conference, namely DUC-2003, DUC-2004, DUC-2005, DUC-2006, and DUC-2007. The ROUGE score is used to assess the generated summaries.