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
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
| Original language | American English |
|---|---|
| Title of host publication | Sci-K 2022 |
| Publisher | Association for Computing Machinery |
| ISBN (Electronic) | 978-1-4503-9130-6/22/04 |
| ISBN (Print) | 978-1-4503-9130-6/22/04 |
| DOIs | |
| State | Published - 2022 |