Comparison of Matrix Factorization and Graph-Based Models for Summary Extraction

With increasing number of users contributing in digital text generation, it becomes necessary to have some tools to extract relevant information from these text documents. Manually performing this task is a time consuming and tidious process. Both Matrix Factorization Based and Graph-Based Models are unsupervised models for extracting a summary from text documents. As the training phase is not required for both these methods, they are extremely fast. Both these methods construct an intermediate representation of a text document and use it to assign a score to each sentence present in the text document. In this paper, we discussed the underlying concept behind both the methods and compare them on the basis of quality of summary extracted.