## Extractive Text Summarization using KMeans
The explosion in the availability of textual data prompts the need to devise a means to effectively compress textual data - Text Summarization. The project aims to deliver an effective text summarizer without comprimizing the intended semantics of the raw data. The proposed system employs clusting the Cosine-Similarity Matrix and sentence extraction from the each cluster.
## Proposed System
1. Data Preprocessing
- Tokenizing
- Stopword Removal
- Lemmatization
2. Feature Extraction
- Conversion of the input text to a TF.IDF matrix
3. Freature Tranformation
- Transforming the TF.IDF matrix to a Cosine-Similarity Matrix
4. Clustering
- K-Means
5. Sentence Extraction
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