  Existing topic modeling and text segmentation methodologies generally require
large datasets for training, limiting their capabilities when only small
collections of text are available. In this work, we reexamine the inter-related
problems of "topic identification" and "text segmentation" for sparse document
learning, when there is a single new text of interest. In developing a
methodology to handle single documents, we face two major challenges. First is
sparse information: with access to only one document, we cannot train
traditional topic models or deep learning algorithms. Second is significant
noise: a considerable portion of words in any single document will produce only
noise and not help discern topics or segments. To tackle these issues, we
design an unsupervised, computationally efficient methodology called BATS:
Biclustering Approach to Topic modeling and Segmentation. BATS leverages three
key ideas to simultaneously identify topics and segment text: (i) a new
mechanism that uses word order information to reduce sample complexity, (ii) a
statistically sound graph-based biclustering technique that identifies latent
structures of words and sentences, and (iii) a collection of effective
heuristics that remove noise words and award important words to further improve
performance. Experiments on four datasets show that our approach outperforms
several state-of-the-art baselines when considering topic coherence, topic
diversity, segmentation, and runtime comparison metrics.
