Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Biomedical text summarization using Conditional Generative Adversarial
  Network(CGAN)   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.

37
The categories of this paper are: supr-con
34
The categories of this paper are: chem-ph
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
6
The categories of this paper are: astro-ph
14
The categories of this paper are: math-ph
33
The categories of this paper are: plasm-ph
2
The categories of this paper are: math
8
The categories of this paper are: hep-ex
32
The categories of this paper are: atom-ph
18
The categories of this paper are: econ
11
The categories of this paper are: hep-lat
31
The categories of this paper are: ao-sci
25
The categories of this paper are: patt-sol
0
The categories of this paper are: hep-ph
21
The categories of this paper are: adap-org