Extractive Text Summarization using Neural Networks
This work addresses the problem of automated text summarization for researchers and practitioners, but it is incremental as it applies a standard neural network method to a known task.
The authors tackled extractive text summarization by proposing a fully data-driven feedforward neural network approach, achieving results comparable to state-of-the-art models on the DUC 2002 dataset.
Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. The proposed model is scalable and is able to produce the summary of arbitrarily sized documents by breaking the original document into fixed sized parts and then feeding it recursively to the network.