I am just curious to know if there is a way to automatically get the lables for the topics in Topic modelling. It would be really helpful if there's any python implementation of it.
Topic modelling is an unsupervised task, so by definition there is no gold-standard label. The task is a kind of clustering, i.e. it tries to group together documents with similar topics, but it doesn't label the groups.
Instead people usually use the words which are the most associated with a topic by the model as a kind of description for the topic.
Usually, the topic modelling algorithm provides a set of topics in which each topic is a collection of terms with the same semantic meaning. By default, the topics are not represented by labels. Most users choose the first word to represent that topic. I would suggest considering the first 5 words to represent that particular topic collection. This may help to get the overall insight into that topic.