Topic modeling is used in information retrieval to infer the hidden themes in a collection of
documents and thus provides an automatic means to organize, understand and summarize …

The goal of text-to-text generation is to make machines express like a human in many
applications such as conversation, summarization, and translation. It is one of the most …

Topic models extract groups of words from documents, whose interpretation as a topic
hopefully allows for a better understanding of the data. However, the resulting word groups …

Topic modelling has been a successful technique for text analysis for almost twenty years.
When topic modelling met deep neural networks, there emerged a new and increasingly …

In recent years, pre-trained large language models (LLMs) have demonstrated remarkable
efficiency in achieving an inference-time few-shot learning capability known as in-context …

Amortized variational inference (AVI) replaces instance-specific local inference with a global
inference network. While AVI has enabled efficient training of deep generative models such …

In the era of social networks, the rapid growth of data mining in information retrieval and
natural language processing makes automatic text summarization necessary. Currently …

Many classification models work poorly on short texts due to data sparsity. To address this
issue, we propose topic memory networks for short text classification with a novel topic …

Social media platforms such as (Twitter, Facebook, and Weibo) are being increasingly
embraced by individuals, groups, and organizations as a valuable source of information …

Recent empirical studies show that adversarial topic models (ATM) can successfully capture
semantic patterns of the document by differentiating a document with another dissimilar …