mlsanityData to 7 Oct 2026

Sequence-Based Extractive Summarisation for Scientific Articles

arXiv 2204.03301 ↗First posted 7 Apr 2022cs.CL cs.IRcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 19 of its 48 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

19 of 48 references.

References · 19

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  4. 2018Multi-Task Identification of Entities, Relations, and Coreference for Scientific Knowledge Graph Constructioncited by 321
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  9. 2017SemEval 2017 Task 10: ScienceIE - Extracting Keyphrases and Relations from Scientific Publicationscited by 101
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  12. 2018Content Selection in Deep Learning Models of Summarizationcited by 64
7 more
  1. 2020Joint Entity and Relation Extraction with Set Prediction Networkscited by 54
  2. 2017A Supervised Approach to Extractive Summarisation of Scientific Paperscited by 33
  3. 2017Scientific Information Extraction with Semi-supervised Neural Taggingcited by 24
  4. 2019The CL-SciSumm Shared Task 2018: Results and Key Insightscited by 9
  5. 2019Biomedical relation extraction with pre-trained language representations and minimal task-specific architecturecited by 8
  6. 2017Neural Extractive Summarization with Side Informationcited by 6
  7. 2017Structure in scientific networks: towards predictions of research dynamismcited by 2

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Citing papers per year: 2023: 1; 2024: 2.

3 papers.

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