Towards an academic abstract sentence classification system

Connor Stead; Stephen Smith; Peter Busch; Savanid Vatanasakdakul

doi:10.1007/978-3-030-50316-1_39

Towards an academic abstract sentence classification system

Connor Stead, Stephen Smith, Peter Busch, Savanid Vatanasakdakul

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

2 Citations (Scopus)

Abstract

This research in progress paper introduces a novel academic abstract sentence classification system intended to improve researcher literature discovery efficiency. The system provides three key functions: 1) displays abstracts with visual identification of each sentence’s indicated literature characteristic class, 2) conversion of unstructured abstracts into structured variants and 3) categorised class sentence extraction available for export to CSV alongside literature metadata. This functionality is made possible by a web application connected to a Python instance via PHP, integration with an open access literature index via an API and a deployed academic abstract sentence classification model. The contribution of the proposed system is its ability to enhance researcher literature discovery. This paper provides context and motivation behind the development of the system, outlines its functionality and provides an outlook for future research.

Original language	English
Title of host publication	Research Challenges in Information Science
Subtitle of host publication	14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings
Editors	Fabiano Dalpiaz, Jelena Zdravkovic, Pericles Loucopoulos
Place of Publication	Cham, Switzerland
Publisher	Springer, Springer Nature
Pages	562-568
Number of pages	7
ISBN (Electronic)	9783030503161
ISBN (Print)	9783030503154
DOIs	https://doi.org/10.1007/978-3-030-50316-1_39
Publication status	Published - 2020
Event	14th International Conference on Research Challenges in Information Science - Limassol, Cyprus, Limassol, Cyprus Duration: 20 May 2020 → 22 May 2020 http://www.rcis-conf.com/rcis2020/

Publication series

Name	Lecture Notes in Business Information Processing
Volume	385
ISSN (Print)	1865-1348
ISSN (Electronic)	1865-1356

Conference

Conference	14th International Conference on Research Challenges in Information Science
Abbreviated title	RCIS 2020
Country/Territory	Cyprus
City	Limassol
Period	20/05/20 → 22/05/20
Internet address	http://www.rcis-conf.com/rcis2020/

Keywords

Abstracts
literature discovery
academic abstract sentence classification

Access to Document

10.1007/978-3-030-50316-1_39

Cite this

Stead, C., Smith, S., Busch, P., & Vatanasakdakul, S. (2020). Towards an academic abstract sentence classification system. In F. Dalpiaz, J. Zdravkovic, & P. Loucopoulos (Eds.), Research Challenges in Information Science: 14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings (pp. 562-568). (Lecture Notes in Business Information Processing; Vol. 385). Springer, Springer Nature. https://doi.org/10.1007/978-3-030-50316-1_39

Stead, Connor ; Smith, Stephen ; Busch, Peter et al. / Towards an academic abstract sentence classification system. Research Challenges in Information Science: 14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings. editor / Fabiano Dalpiaz ; Jelena Zdravkovic ; Pericles Loucopoulos. Cham, Switzerland : Springer, Springer Nature, 2020. pp. 562-568 (Lecture Notes in Business Information Processing).

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abstract = "This research in progress paper introduces a novel academic abstract sentence classification system intended to improve researcher literature discovery efficiency. The system provides three key functions: 1) displays abstracts with visual identification of each sentence{\textquoteright}s indicated literature characteristic class, 2) conversion of unstructured abstracts into structured variants and 3) categorised class sentence extraction available for export to CSV alongside literature metadata. This functionality is made possible by a web application connected to a Python instance via PHP, integration with an open access literature index via an API and a deployed academic abstract sentence classification model. The contribution of the proposed system is its ability to enhance researcher literature discovery. This paper provides context and motivation behind the development of the system, outlines its functionality and provides an outlook for future research.",

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editor = "Fabiano Dalpiaz and Jelena Zdravkovic and Pericles Loucopoulos",

booktitle = "Research Challenges in Information Science",

address = "United States",

note = "14th International Conference on Research Challenges in Information Science, RCIS 2020 ; Conference date: 20-05-2020 Through 22-05-2020",

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Stead, C, Smith, S , Busch, P & Vatanasakdakul, S 2020, Towards an academic abstract sentence classification system. in F Dalpiaz, J Zdravkovic & P Loucopoulos (eds), Research Challenges in Information Science: 14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings. Lecture Notes in Business Information Processing, vol. 385, Springer, Springer Nature, Cham, Switzerland, pp. 562-568, 14th International Conference on Research Challenges in Information Science, Limassol, Cyprus, 20/05/20. https://doi.org/10.1007/978-3-030-50316-1_39

Towards an academic abstract sentence classification system. / Stead, Connor; Smith, Stephen ; Busch, Peter et al.
Research Challenges in Information Science: 14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings. ed. / Fabiano Dalpiaz; Jelena Zdravkovic; Pericles Loucopoulos. Cham, Switzerland: Springer, Springer Nature, 2020. p. 562-568 (Lecture Notes in Business Information Processing; Vol. 385).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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AU - Busch, Peter

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AB - This research in progress paper introduces a novel academic abstract sentence classification system intended to improve researcher literature discovery efficiency. The system provides three key functions: 1) displays abstracts with visual identification of each sentence’s indicated literature characteristic class, 2) conversion of unstructured abstracts into structured variants and 3) categorised class sentence extraction available for export to CSV alongside literature metadata. This functionality is made possible by a web application connected to a Python instance via PHP, integration with an open access literature index via an API and a deployed academic abstract sentence classification model. The contribution of the proposed system is its ability to enhance researcher literature discovery. This paper provides context and motivation behind the development of the system, outlines its functionality and provides an outlook for future research.

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BT - Research Challenges in Information Science

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Stead C, Smith S , Busch P, Vatanasakdakul S. Towards an academic abstract sentence classification system. In Dalpiaz F, Zdravkovic J, Loucopoulos P, editors, Research Challenges in Information Science: 14th International Conference, RCIS 2020, Limassol, Cyprus, September 23–25, 2020, Proceedings. Cham, Switzerland: Springer, Springer Nature. 2020. p. 562-568. (Lecture Notes in Business Information Processing). doi: 10.1007/978-3-030-50316-1_39

Towards an academic abstract sentence classification system

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