Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)

Abstract

Text summarization in medicine can help doctors for reducing the time to access important information from countless documents. The paper offers a supervised extractive summarization method based on conditional generative adversarial networks using convolutional neural networks. Unlike previous models, which often use greedy methods to select sentences, we use a new approach for selecting sentences. Moreover, we provide a network for biomedical word embedding, which improves summarization. An essential contribution of the paper is introducing a new loss function for the discriminator, making the discriminator perform better. The proposed model achieves results comparable to the state-of-the-art approaches, as determined by the ROUGE metric. Experiments on the medical dataset show that the proposed method works on average 5% better than the competing models and is more similar to the reference summaries.