I. Statistical Approaches

A. Frequency-based Methods

1. Sentence Frequency
2. Word Frequency
3. TF-IDF (Term Frequency-Inverse Document Frequency)

B. Latent Semantic Analysis (LSA)

C. Latent Dirichlet Allocation (LDA)

II. Graph-based Approaches

A. Centrality-based Methods

1. Degree Centrality
2. PageRank
3. Betweenness Centrality

B. Cluster-based Methods

1. K-Means Clustering
2. Hierarchical Clustering
3. TextTiling

III. Machine Learning Approaches

A. Supervised Learning Methods

1. Support Vector Machines (SVM)
2. Decision Trees
3. Random Forests

B. Unsupervised Learning Methods

1. K-Nearest Neighbors (KNN)
2. Neural Networks
3. Deep Learning

IV. Hybrid Approaches

A. Combining Statistical and Graph-based Methods B. Combining Statistical and Machine Learning Methods C. Combining Graph-based and Machine Learning Methods

V. Other Approaches A. Lexical Chain Approach B. Genetic Algorithm Approach C. Sentence Compression Approach