  Deep learning architectures based on self-attention have recently achieved
and surpassed state of the art results in the task of unsupervised aspect
extraction and topic modeling. While models such as neural attention-based
aspect extraction (ABAE) have been successfully applied to user-generated
texts, they are less coherent when applied to traditional data sources such as
news articles and newsgroup documents. In this work, we introduce a simple
approach based on sentence filtering in order to improve topical aspects
learned from newsgroups-based content without modifying the basic mechanism of
ABAE. We train a probabilistic classifier to distinguish between out-of-domain
texts (outer dataset) and in-domain texts (target dataset). Then, during data
preparation we filter out sentences that have a low probability of being
in-domain and train the neural model on the remaining sentences. The positive
effect of sentence filtering on topic coherence is demonstrated in comparison
to aspect extraction models trained on unfiltered texts.
