  A scientific paper is traditionally prefaced by an abstract that summarizes
the paper. Recently, research highlights that focus on the main findings of the
paper have emerged as a complementary summary in addition to an abstract.
However, highlights are not yet as common as abstracts, and are absent in many
papers. In this paper, we aim to automatically generate research highlights
using different sections of a research paper as input. We investigate whether
the use of named entity recognition on the input improves the quality of the
generated highlights. In particular, we have used two deep learning-based
models: the first is a pointer-generator network, and the second augments the
first model with coverage mechanism. We then augment each of the above models
with named entity recognition features. The proposed method can be used to
produce highlights for papers with missing highlights. Our experiments show
that adding named entity information improves the performance of the deep
learning-based summarizers in terms of ROUGE, METEOR and BERTScore measures.
