  Presentation slides describing the content of scientific and technical papers
are an efficient and effective way to present that work. However, manually
generating presentation slides is labor intensive. We propose a method to
automatically generate slides for scientific papers based on a corpus of 5000
paper-slide pairs compiled from conference proceedings websites. The sentence
labeling module of our method is based on SummaRuNNer, a neural sequence model
for extractive summarization. Instead of ranking sentences based on semantic
similarities in the whole document, our algorithm measures importance and
novelty of sentences by combining semantic and lexical features within a
sentence window. Our method outperforms several baseline methods including
SummaRuNNer by a significant margin in terms of ROUGE score.
