Systematic T ext R ank Optimization in Extractive Summarization (2023)

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.

## introduction in the modern era, the sheer volume of data generated daily poses a significant challenge for decisionmakers to stay informed about the latest trends and developments. text summarization addresses this issue by extracting only the most salient information from a text. this study investigates the effectiveness of textrank, an extractive text summarization algorithm, compared to other common approaches, such as abstractive and hybrid summarizations. automatic text summarization can be classified based on the input size, algorithm, content, domain, language, type, and approach @xcite . one approach is extractive summarization, which selects essential sentences from the input document(s) and concatenates them to form the summary. another approach is abstractive summarization, which creates an intermediate representation of the input document(s) and generates a summary. lastly, hybrid summarization combines extractive and abstractive approaches @xcite . extractive text summarization is a widelyused approach in natural language processing (nlp) that aims to condense large volumes of text into shorter, more manageable versions. this method involves selecting the most relevant sentences or phrases from the source text and combining them to create a summary that accurately conveys the essential information and main ideas of the source material @xcite . abstractive text summarization is an advanced text summarization approach that employs nlp techniques to generate concise sentences that accurately convey the main ideas of the original text. this technique can benefit various domains where decision-makers require a rapid understanding of a document's primary points. abstractive summarization can produce more coherent and efficient documents by eliminating redundancy and repetition. unlike extractive text summarization, which selects and combines existing sentences or phrases, abstractive text summarization generates new and concise sentences, making it more versatile and flexible @xcite . hybrid text summarization combines extractive and abstractive text summarization strengths, resulting in a robust approach for condensing large volumes of text into shorter, more understandable versions. this technique minimizes word repetition and enhances the model's accuracy, necessitating ongoing refinement and experimentation to fine-tune the system and optimize its performance @xcite . ## related work a recent study presented an nlp-based approach to generate business meeting summaries @xcite . this research proposed a methodology employing various nlp techniques, such as named entity recognition (ner), to identify crit-ical entities. moreover, the authors utilized the "textrank" algorithm, based on "pagerank", to rank meaningful sentences and generate summaries according to the sentence rankings. this proposed methodology belongs to the extractive text summarization category. the approach demonstrated promising results in extracting vital information from business meetings and generating summaries that capture the meetings' main ideas. the application of nlp techniques for summarizing text data, including a transcribed speech from meetings or extracting critical details from articles, has increased interest. another recent study @xcite explores the topic of summarizing meeting transcripts from google meet. this study investigates the effectiveness of various nlp models for summarizing transcripts and compares several models using metrics such as rouge. the study offers insights into the performance of different nlp models for extractive and abstractive summarization tasks. building upon the insights from these studies, our proposed methodology introduces an enhanced textrank approach using cosine similarity for ngrams and fine-tuning hyperparameters. by addressing various pre-processing states, fine-tuning of textrank, an intended combination of nlp summarization models into a hybrid model, and calculating the evaluation metrics using rouge scores widely used in previous research, to ensure a fair comparison with existing methods. we aim to improve extractive summarization's overall performance and accuracy by taking these steps. the reviewed literature provides a solid foundation for our proposed methodology, as it leverages state-of-the-art nlp techniques and insights gained from previous research, such as using textrank to achieve the highest accuracy. ## methodology our methodology employs a textrank algorithm enhanced with cosine similarity for n-grams and fine-tuned hyperparameters to achieve optimal performance. this approach consists of four critical stages: preprocessing, fine-tuning textrank, generating the summary, and evaluating the results using rouge scores, as shown in figure 1 . ## conclusions this study proposes a refined approach to the tex-trank model for extractive text summarization. our methodology outperforms the existing tex-trank method @xcite and the nltk extractive model @xcite on various rouge metrics.