A Comparative Analysis of TextRank and LexRank Algorithms Using Text Summarization

Text summarizing has grown in importance and popularity as a way to retain and highlight the important points of textual information as there is a growth of online information and resource texts. Large texts are particularly challenging for humans to manually summarize. Text summarizing is the process of automatically producing and compressing a given document's form while maintaining the information content source to produce a more concise version with a clearer message. This paper compares two widely used extractive text summarization algorithms namely TextRank and LexRank. This paper provides a detailed overview and comparison of both approaches and also provides information to understand which algorithm to use when necessary. TextRank and LexRank, both algorithms are used in natural language processing for automated text summarization, but they differ in their approaches and implementations.