@InProceedings{collins-augenstein-riedel:2017:CoNLL,
  author    = {Collins, Ed  and  Augenstein, Isabelle  and  Riedel, Sebastian},
  title     = {A Supervised Approach to Extractive Summarisation of Scientific Papers},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {195--205},
  abstract  = {Automatic summarisation is a popular approach to reduce a document to its main
	arguments. Recent research in the area has focused on neural approaches to
	summarisation, which can be very data-hungry. However, few large datasets exist
	and none for the traditionally popular domain of scientific publications, which
	opens up challenging research avenues centered on encoding large, complex
	documents. In this paper, we introduce a new dataset for summarisation of
	computer science publications by exploiting a large resource of author provided
	summaries and show straightforward ways of extending it further. We develop
	models on the dataset making use of both neural sentence encoding and
	traditionally used summarisation features and show that models which encode
	sentences as well as their local and global context perform best, significantly
	outperforming well-established baseline methods.},
  url       = {http://aclweb.org/anthology/K17-1021}
}