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Titles of short sections within long documents support readers by guidingtheir focus towards relevant passages and by providing anchor-points that helpto understand the progression of the document. The positive effects of sectiontitles are even more pronounced when measured on readers with less developedreading abilities, for example in communities with limited labeled textresources.  We, therefore, aim to develop techniques to generate section titles inlow-resource environments. In particular, we present an extractive pipeline forsection title generation by first selecting the most salient sentence and thenapplying deletion-based compression. Our compression approach is based on aSemi-Markov Conditional Random Field that leverages unsupervisedword-representations such as ELMo or BERT, eliminating the need for a complexencoder-decoder architecture. The results show that this approach leads tocompetitive performance with sequence-to-sequence models with high resources,while strongly outperforming it with low resources. In a human-subject studyacross subjects with varying reading abilities, we find that our section titlesimprove the speed of completing comprehension tasks while retaining similaraccuracy.

Improving Human Text Comprehension through Semi-Markov CRF-based Neural Section Title Generation