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.
CITATION STYLE
Chen, Y., Polajnar, T., Batchelor, C., & Teufel, S. (2020). A Corpus of Very Short Scientific Summaries. In CoNLL 2020 - 24th Conference on Computational Natural Language Learning, Proceedings of the Conference (pp. 153–164). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/2020.conll-1.12
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