TY  - GEN
SP  - 195
PB  - Association for Computational Linguistics (ACL)
N1  - © 1963?2019 ACL; other materials are copyrighted by their respective copyright holders. Materials prior to 2016 here are licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 International License. Permission is granted to make copies for the purposes of teaching and research. Materials published in or after 2016 are licensed on a Creative Commons Attribution 4.0 International License.
Y1  - 2017/08/04/
TI  - A supervised approach to extractive summarisation of scientific papers
N2  - 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.
AV  - public
EP  - 205
A1  - Collins, E
A1  - Augenstein, I
A1  - Riedel, S
UR  - https://doi.org/10.18653/v1/K17-1021
CY  - Vancouver, Canada
ID  - discovery10084687
ER  -