### TextRank **TextRank** is a graph-based ranking model for text processing, inspired by Google's PageRank. In the context of NLP, TextRank is used for text summarization and keyword extraction. The algorithm treats sentences or words as nodes in a graph and builds connections between them based on their similarity or co-occurrence. It then ranks these nodes to identify the most important sentences for summarization or key terms for keyword extraction. TextRank is unsupervised and can capture more nuanced and contextually significant keywords through its graph-based approach. ### How TextRank Works: TextRank builds upon the concept of the PageRank algorithm, which Google uses to rank web pages. TextRank constructs a graph for a given text, where: - **For text summarization:** Sentences are represented as nodes, and the edges between them are based on their similarity. A sentence that is similar to many others is considered important and is more likely to be included in the summary. - **For keyword extraction:** Words or phrases are nodes, and connections (edges) between them are based on their co-occurrence within a certain distance from each other in the text. Highly connected nodes are considered keywords. **Pros:** - Captures contextual significance and the relational importance of words. - Language-independent and unsupervised, requiring no training data. - Can be extended or modified to improve performance on specific types of texts. **Cons:** - Higher computational cost due to graph construction and ranking algorithm. - May not always capture the most relevant keywords in very short texts or texts with sparse keyword occurrences.