@inproceedings{2fff12a6d3204fb9ac66be9922762ed0,
title = "Sequence-Based Extractive Summarisation for Scientific Articles",
abstract = "This paper presents the results of research on supervised extractive text summarisation for scientific articles. We show that a simple sequential tagging model based only on the text within a document achieves high results against a simple classification model. Improvements can be achieved through additional sentence-level features, though these were minimal. Through further analysis, we show the potential of the sequential model relying on the structure of the document depending on the academic discipline which the document is from.",
keywords = "corpus, extractive, neural networks, scientific texts, summerisation",
author = "Daniel Kershaw and Rob Koeling",
note = "Publisher Copyright: {\textcopyright} 2022 ACM.; 31st Companion of the World Wide Web Conference, WWW 2022 ; Conference date: 25-04-2022",
year = "2022",
month = aug,
day = "16",
doi = "10.1145/3487553.3524652",
language = "Ingl{\'e}s",
series = "WWW 2022 - Companion Proceedings of the Web Conference 2022",
publisher = "Association for Computing Machinery, Inc",
pages = "751--757",
booktitle = "WWW 2022 - Companion Proceedings of the Web Conference 2022",
}