Biomedical text summarization using Conditional Generative Adversarial Network(CGAN)
This work addresses the problem of reducing time for doctors to access key information from medical documents, representing an incremental advance in domain-specific summarization.
The paper tackles biomedical text summarization by proposing a supervised extractive method using conditional generative adversarial networks (CGAN) with a new sentence selection approach and biomedical word embeddings, achieving results comparable to state-of-the-art and an average 5% improvement over competing models on a medical dataset.
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.