Abstract
We present a new summarisation task, taking scientific articles and producing journal table-of-contents entries in the chemistry domain. These are one- or two-sentence author-written summaries that present the key findings of a paper. This is a first look at this summarisation task with an open access publication corpus consisting of titles and abstracts, as input texts, and short author-written advertising blurbs, as the ground truth. We introduce the dataset and evaluate it with state-of-the-art summarisation methods.- Anthology ID:
- 2020.conll-1.12
- Volume:
- Proceedings of the 24th Conference on Computational Natural Language Learning
- Month:
- November
- Year:
- 2020
- Address:
- Online
- Venue:
- CoNLL
- SIG:
- SIGNLL
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 153–164
- Language:
- URL:
- https://aclanthology.org/2020.conll-1.12
- DOI:
- 10.18653/v1/2020.conll-1.12
- Cite (ACL):
- Yifan Chen, Tamara Polajnar, Colin Batchelor, and Simone Teufel. 2020. A Corpus of Very Short Scientific Summaries. In Proceedings of the 24th Conference on Computational Natural Language Learning, pages 153–164, Online. Association for Computational Linguistics.
- Cite (Informal):
- A Corpus of Very Short Scientific Summaries (Chen et al., CoNLL 2020)
- PDF:
- https://preview.aclanthology.org/remove-xml-comments/2020.conll-1.12.pdf
- Code
- atulkum/pointer_summarizer