@inproceedings{hewett-stede-2022-extractive,
title = "Extractive Summarisation for {G}erman-language Data: A Text-level Approach with Discourse Features",
author = "Hewett, Freya and
Stede, Manfred",
booktitle = "Proceedings of the 29th International Conference on Computational Linguistics",
month = oct,
year = "2022",
address = "Gyeongju, Republic of Korea",
publisher = "International Committee on Computational Linguistics",
url = "https://aclanthology.org/2022.coling-1.63",
pages = "756--765",
abstract = "We examine the link between facets of Rhetorical Structure Theory (RST) and the selection of content for extractive summarisation, for German-language texts. For this purpose, we produce a set of extractive summaries for a dataset of German-language newspaper commentaries, a corpus which already has several layers of annotation. We provide an in-depth analysis of the connection between summary sentences and several RST-based features and transfer these insights to various automated summarisation models. Our results show that RST features are informative for the task of extractive summarisation, particularly nuclearity and relations at sentence-level.",
}
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%0 Conference Proceedings
%T Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features
%A Hewett, Freya
%A Stede, Manfred
%S Proceedings of the 29th International Conference on Computational Linguistics
%D 2022
%8 October
%I International Committee on Computational Linguistics
%C Gyeongju, Republic of Korea
%F hewett-stede-2022-extractive
%X We examine the link between facets of Rhetorical Structure Theory (RST) and the selection of content for extractive summarisation, for German-language texts. For this purpose, we produce a set of extractive summaries for a dataset of German-language newspaper commentaries, a corpus which already has several layers of annotation. We provide an in-depth analysis of the connection between summary sentences and several RST-based features and transfer these insights to various automated summarisation models. Our results show that RST features are informative for the task of extractive summarisation, particularly nuclearity and relations at sentence-level.
%U https://aclanthology.org/2022.coling-1.63
%P 756-765
Markdown (Informal)
[Extractive Summarisation for German-language Data: A Text-level Approach with Discourse Features](https://aclanthology.org/2022.coling-1.63) (Hewett & Stede, COLING 2022)
ACL