Automatic Text Summarization Using LSTM Based...

Automatic Text Summarization Using LSTM Based On Sentence Semantics

Author Name : Kalangi Praveen Kumar*, T.N.S. Padma, B. Pruthvi Raj Goud

ABSTRACT TEXT SUMMARIZATION which is a new technique for summarizing news articles using a neural network is presented. A neural network is trained to learn the relevant characteristics of sentences that should be included in the summary of the article. The neural network is then modified to generalize and combine the relevant characteristics apparent in summary sentences. Finally, the modified neural network is used as a filter to summarize news articles. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feed forward neural networks for single document summarization. We train and evaluate the model on dataset which shows results comparable to the state-of-the-art models. There are three phases in our process: neural network training, feature fusion, and sentence selection. The first step involves training a neural network to recognize the type of sentences that should be included in the summary. The second step, feature fusion, prunes the neural network and collapses the hidden layer unit activations into discrete values with frequencies. This step generalizes the important features that must exist in the summary sentences by fusing the features and finding trends in the summary sentences. The third step, sentence selection, uses the modified neural network to filter the text and to select only the highly ranked sentences. This step controls the selection of the summary sentences in terms of their importance.