Systematic TextRank Optimization in Extractive Summarization
Morris Zieve, Anthony Gregor, Frederik Juul Stokbaek, Hunter Lewis, Ellis Marie Mendoza, Benyamin Ahmadnia
Abstract
With the ever-growing amount of textual data, extractive summarization has become increasingly crucial for efficiently processing information.The TextRank algorithm, a popular unsupervised method, offers excellent potential for this task.In this paper, we aim to optimize the performance of TextRank by systematically exploring and verifying the best preprocessing and fine-tuning techniques.We extensively evaluate text preprocessing methods, such as tokenization, stemming, and stopword removal, to identify the most effective combination with TextRank.Additionally, we examine fine-tuning strategies, including parameter optimization and incorporation of domain-specific knowledge, to achieve superior summarization quality.