  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.
