  When searching for information, a human reader first glances over a document,
spots relevant sections and then focuses on a few sentences for resolving her
intention. However, the high variance of document structure complicates to
identify the salient topic of a given section at a glance. To tackle this
challenge, we present SECTOR, a model to support machine reading systems by
segmenting documents into coherent sections and assigning topic labels to each
section. Our deep neural network architecture learns a latent topic embedding
over the course of a document. This can be leveraged to classify local topics
from plain text and segment a document at topic shifts. In addition, we
contribute WikiSection, a publicly available dataset with 242k labeled sections
in English and German from two distinct domains: diseases and cities. From our
extensive evaluation of 20 architectures, we report a highest score of 71.6% F1
for the segmentation and classification of 30 topics from the English city
domain, scored by our SECTOR LSTM model with bloom filter embeddings and
bidirectional segmentation. This is a significant improvement of 29.5 points F1
compared to state-of-the-art CNN classifiers with baseline segmentation.
