  We develop models and extract relevant features for automatic text
summarization and investigate the performance of different models on the DUC
2001 dataset. Two different models were developed, one being a ridge regressor
and the other one was a multi-layer perceptron. The hyperparameters were varied
and their performance were noted. We segregated the summarization task into 2
main steps, the first being sentence ranking and the second step being sentence
selection. In the first step, given a document, we sort the sentences based on
their Importance, and in the second step, in order to obtain non-redundant
sentences, we weed out the sentences that are have high similarity with the
previously selected sentences.
