  Titles of short sections within long documents support readers by guiding
their focus towards relevant passages and by providing anchor-points that help
to understand the progression of the document. The positive effects of section
titles are even more pronounced when measured on readers with less developed
reading abilities, for example in communities with limited labeled text
resources.
  We, therefore, aim to develop techniques to generate section titles in
low-resource environments. In particular, we present an extractive pipeline for
section title generation by first selecting the most salient sentence and then
applying deletion-based compression. Our compression approach is based on a
Semi-Markov Conditional Random Field that leverages unsupervised
word-representations such as ELMo or BERT, eliminating the need for a complex
encoder-decoder architecture. The results show that this approach leads to
competitive performance with sequence-to-sequence models with high resources,
while strongly outperforming it with low resources. In a human-subject study
across subjects with varying reading abilities, we find that our section titles
improve the speed of completing comprehension tasks while retaining similar
accuracy.
