Collins, E;
Augenstein, I;
Riedel, S;
(2017)
A supervised approach to extractive summarisation of scientific papers.
In:
Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017).
(pp. pp. 195-205).
Association for Computational Linguistics (ACL): Vancouver, Canada.
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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.
| Type: | Proceedings paper |
|---|---|
| Title: | A supervised approach to extractive summarisation of scientific papers |
| Event: | 21st Conference on Computational Natural Language Learning (CoNLL 2017) |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.18653/v1/K17-1021 |
| Publisher version: | https://doi.org/10.18653/v1/K17-1021 |
| Language: | English |
| Additional information: | © 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. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10084687 |
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