  In this research work, we present a method to generate summaries of long
scientific documents that uses the advantages of both extractive and
abstractive approaches. Before producing a summary in an abstractive manner, we
perform the extractive step, which then is used for conditioning the abstractor
module. We used pre-trained transformer-based language models, for both
extractor and abstractor. Our experiments showed that using extractive and
abstractive models jointly significantly improves summarization results and
ROUGE scores.
