  Recent neural supervised topic segmentation models achieve distinguished
superior effectiveness over unsupervised methods, with the availability of
large-scale training corpora sampled from Wikipedia. These models may, however,
suffer from limited robustness and transferability caused by exploiting simple
linguistic cues for prediction, but overlooking more important inter-sentential
topical consistency. To address this issue, we present a discourse-aware neural
topic segmentation model with the injection of above-sentence discourse
dependency structures to encourage the model make topic boundary prediction
based more on the topical consistency between sentences. Our empirical study on
English evaluation datasets shows that injecting above-sentence discourse
structures to a neural topic segmenter with our proposed strategy can
substantially improve its performances on intra-domain and out-of-domain data,
with little increase of model's complexity.
