  The emerging neural topic models make topic modeling more easily adaptable
and extendable in unsupervised text mining. However, the existing neural topic
models is difficult to retain representative information of the documents
within the learnt topic representation. In this paper, we propose a neural
topic model which incorporates deep mutual information estimation, i.e., Neural
Topic Modeling with Deep Mutual Information Estimation(NTM-DMIE). NTM-DMIE is a
neural network method for topic learning which maximizes the mutual information
between the input documents and their latent topic representation. To learn
robust topic representation, we incorporate the discriminator to discriminate
negative examples and positive examples via adversarial learning. Moreover, we
use both global and local mutual information to preserve the rich information
of the input documents in the topic representation. We evaluate NTM-DMIE on
several metrics, including accuracy of text clustering, with topic
representation, topic uniqueness and topic coherence. Compared to the existing
methods, the experimental results show that NTM-DMIE can outperform in all the
metrics on the four datasets.
