## 2 Related Work  
Extractive text summarization (ETS) has undergone substantial evolution, driven by advancements in both computational capacities and theoretical understanding. This section provides an overview of the progression in ETS techniques, from early statistical methods to contemporary machine learning approaches, and discusses the integration of novel computational models that underpin our proposed framework. Traditional ETS methods predominantly utilize statistical metrics to identify key sentences within a document. These methods emphasize keyword frequency and sentence positioning to gauge importance, as evidenced by the pioneering work on algorithms like TF-IDF [18]. Although foundational, these approaches often fail to capture the nuanced semantic relationships within texts, as they lack the capacity to consider broader discourse structures and thematic coherence. The introduction of machine learning algorithms marked a significant shift in summarization research. Techniques such as fuzzy logic and machine learning classifiers began to be applied to enhance summarization quality by addressing the ambiguities inherent in human language. For instance, the use of the fuzzy c-means clustering algorithm alongside TF-IDF was explored to categorize sentences by importance [19], which provided a more nuanced approach than simple frequency-based metrics. Graph-based models also became prevalent, with methods like LexRank and TextRank leveraging the relational information among sentences to improve summarization outcomes [20]. These models utilize the concept of ranking to prioritize sentences that are most central within the document's semantic network, illustrating the shift towards understanding inter-sentence relationships. The advent of deep learning has brought transformative changes to ETS. Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), and Transformer-based models like BERT have been employed to process large-scale data and learn intricate patterns within texts [21]. These models excel at capturing contextual nuances and have significantly improved the ability of summarization systems to generate coherent and relevant summaries [22]. For example, recent studies have introduced graph neural networks (GNNs) and heterogeneous graph-based models to ETS [23; 24]. These approaches dynamically incorporate diverse information types from the document's structure, enhancing the summarization process by synthesizing summaries that reflect the document's complex informational hierarchy [25; 26; 27]. Despite the achievements of deep learning models, challenges remain, particularly in integrating document structure and thematic essence coherently, especially in lengthy and complex documents [28; 29]. Hybrid models that combine multiple deep learning strategies with traditional machine learning techniques have been proposed to address these challenges [30]. These models aim to leverage the strengths of each approach, ensuring that summaries are not only precise but also thematically consistent and contextually rich [31]. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference** & **Technology Used** & **Performance/Advantages** & **Disadvantages** \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2019) [1]} & BERT-based & High performance in capturing contextual nuances; improved ROUGE scores & Computationally expensive; requires large datasets for training \\ \hline \multirow{2}{*}{[PERSON] and [PERSON] (2017) [2]} & \multirow{2}{*}{Statistical and linguistic features} & Simple implementation; effective for basic tasks & Fail to capture deep semantic relationships; limited contextual understanding \\ \hline \multirow{3}{*}{[PERSON] and [PERSON] (2010) [3]} & \multirow{3}{*}{TF-IDF, frequency-based methods} & Easy to implement; interpretable results & Do not capture document structure; poor performance on long texts \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of existing work in extractive summarization.