Text Summarization using Deep Learning and Ridge Regression
This work addresses text summarization for document processing, but it is incremental as it applies existing methods to a standard dataset.
The authors tackled automatic text summarization by developing ridge regression and multi-layer perceptron models for sentence ranking and selection, achieving performance evaluated on the DUC 2001 dataset with varied hyperparameters.
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.