A Survey of Text Summarization Extractive Techniques <s> G. Text summarization with neural networks <s> A novel technique for summarizing news articles using neural networks is presented. A neural network is trained to learn the relevant features of sentences that should be included in the summary of the article. The neural network is then modified to generalize and combine the relevant features apparent in summary sentences. Finally, the modified neural network is used as a filter to summarize news articles. <s> BIB001 </s> A Survey of Text Summarization Extractive Techniques <s> G. Text summarization with neural networks <s> Originally published in WSEAS Transactions on Systems, Volume 3, Issue 2, April 2004 (pp ::: 960-963). <s> BIB002
This method involves training the neural networks to learn the types of sentences that should be included in the summary. This is accomplished by training the network with sentences in several test paragraphs where each sentence is identified as to whether it should be included in the summary or not. This is done by a human reader. The neural network BIB001 learns the patterns inherent in sentences that should be included in the summary and those that should not be included. It uses three-layered Feed forward neural network, which has been proven to be a universal function approximator. The first phase of the process involves training the neural networks to learn the types of sentences that should be included in the summary. This is accomplished by training the network with sentences in several test paragraphs where each sentence is identified as to whether it should be included in the summary or not. This is done by a human reader. The neural network learns the patterns inherent in sentences that should be included in the summary and those that should not be included. The Neural Network BIB002 after Training is shown in figure3. Once the network has learned the features that must exist in summary sentences, we need to discover the trends and relationships among the features that are inherent in the majority of sentences. This is accomplished by the feature fusion phase, which consists of two steps: 1) eliminating uncommon features; and 2) collapsing the effects of common features. The connections having very small weights after training can be pruned without affecting the performance of the network. As a result, any input or hidden layer neuron having no emanating connections can be safely removed from the network. In addition, any hidden layer neuron having no abutting connections can be removed. This corresponds to eliminating uncommon features from the network BIB002 as shown in figure4. The hidden layer activation values for each hidden layer neuron are clustered utilizing an adaptive clustering technique. Each cluster is identified by its centroid and frequency. The activation value of each hidden layer neuron is replaced by the centroid of the cluster, which the activation value belongs to. This corresponds to collapsing the effects of common features. The combination of these two steps corresponds to Figure 5 . Neural Network after feature fusion generalizing the effects of features, as a whole, and providing control parameters for sentence ranking. The Neural Network BIB002 after feature fusion is shown in figure5.