  The unsupervised text clustering is one of the major tasks in natural
language processing (NLP) and remains a difficult and complex problem.
Conventional \mbox{methods} generally treat this task using separated steps,
including text representation learning and clustering the representations. As
an improvement, neural methods have also been introduced for continuous
representation learning to address the sparsity problem. However, the
multi-step process still deviates from the unified optimization target.
Especially the second step of cluster is generally performed with conventional
methods such as k-Means. We propose a pure neural framework for text clustering
in an end-to-end manner. It jointly learns the text representation and the
clustering model. Our model works well when the context can be obtained, which
is nearly always the case in the field of NLP. We have our method
\mbox{evaluated} on two widely used benchmarks: IMDB movie reviews for
sentiment classification and $20$-Newsgroup for topic categorization. Despite
its simplicity, experiments show the model outperforms previous clustering
methods by a large margin. Furthermore, the model is also verified on English
wiki dataset as a large corpus.
