A comparative study on question-worthy sentence selection strategies for educational question generation

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

Abstract

Automatic question generation, which aims at converting sentences in an article to high-quality questions, is an important task for educational practices. Recent work mainly focuses on designing effective generation architectures based on deep neural networks. However, the first and possibly the foremost step of automatic question generation has largely been ignored, i.e., identifying sentences carrying important information or knowledge that is worth asking questions about. In this work, we (i) propose a total of 9 strategies, which are grounded on heuristic question-asking assumptions, to determine sentences that are question-worthy, and (ii) compare their performance on 4 datasets by using the identified sentences as input for a well-trained question generator. Through extensive experiments, we show that (i) LexRank, a stochastic graph-based method for selecting important sentences from articles, gives robust performance across all datasets, (ii) questions collected in educational settings feature a more diverse set of source sentences than those obtained in non-educational settings, and (iii) more research efforts are needed to further improve the design of educational question generation architectures.

Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I
EditorsSeiji Isotani, Eva Millán, Amy Ogan, Peter Hastings, Bruce McLaren, Rose Luckin
Place of PublicationCham Switzerland
PublisherSpringer
Pages59-70
Number of pages12
ISBN (Electronic)9783030232047
ISBN (Print)9783030232030
DOIs
Publication statusPublished - 2019
EventInternational Conference on Artificial Intelligence in Education 2019 - Chicago, United States of America
Duration: 25 Jun 201929 Jun 2019
Conference number: 20th
https://iaied.org/

Publication series

NameLecture Notes in Computer Science
PublisherSpringer
Volume11625
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Artificial Intelligence in Education 2019
Abbreviated titleAIED 2019
CountryUnited States of America
CityChicago
Period25/06/1929/06/19
Internet address

Keywords

  • Deep neural network
  • Educational question generation
  • Sentence selection

Cite this

Chen, G., Yang, J., & Gasevic, D. (2019). A comparative study on question-worthy sentence selection strategies for educational question generation. In S. Isotani, E. Millán, A. Ogan, P. Hastings, B. McLaren, & R. Luckin (Eds.), Artificial Intelligence in Education : 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I (pp. 59-70). (Lecture Notes in Computer Science ; Vol. 11625 ). Cham Switzerland: Springer. https://doi.org/10.1007/978-3-030-23204-7_6
Chen, Guanliang ; Yang, Jie ; Gasevic, Dragan. / A comparative study on question-worthy sentence selection strategies for educational question generation. Artificial Intelligence in Education : 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I. editor / Seiji Isotani ; Eva Millán ; Amy Ogan ; Peter Hastings ; Bruce McLaren ; Rose Luckin. Cham Switzerland : Springer, 2019. pp. 59-70 (Lecture Notes in Computer Science ).
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abstract = "Automatic question generation, which aims at converting sentences in an article to high-quality questions, is an important task for educational practices. Recent work mainly focuses on designing effective generation architectures based on deep neural networks. However, the first and possibly the foremost step of automatic question generation has largely been ignored, i.e., identifying sentences carrying important information or knowledge that is worth asking questions about. In this work, we (i) propose a total of 9 strategies, which are grounded on heuristic question-asking assumptions, to determine sentences that are question-worthy, and (ii) compare their performance on 4 datasets by using the identified sentences as input for a well-trained question generator. Through extensive experiments, we show that (i) LexRank, a stochastic graph-based method for selecting important sentences from articles, gives robust performance across all datasets, (ii) questions collected in educational settings feature a more diverse set of source sentences than those obtained in non-educational settings, and (iii) more research efforts are needed to further improve the design of educational question generation architectures.",
keywords = "Deep neural network, Educational question generation, Sentence selection",
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Chen, G, Yang, J & Gasevic, D 2019, A comparative study on question-worthy sentence selection strategies for educational question generation. in S Isotani, E Millán, A Ogan, P Hastings, B McLaren & R Luckin (eds), Artificial Intelligence in Education : 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I. Lecture Notes in Computer Science , vol. 11625 , Springer, Cham Switzerland, pp. 59-70, International Conference on Artificial Intelligence in Education 2019, Chicago, United States of America, 25/06/19. https://doi.org/10.1007/978-3-030-23204-7_6

A comparative study on question-worthy sentence selection strategies for educational question generation. / Chen, Guanliang; Yang, Jie; Gasevic, Dragan.

Artificial Intelligence in Education : 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I. ed. / Seiji Isotani; Eva Millán; Amy Ogan; Peter Hastings; Bruce McLaren; Rose Luckin. Cham Switzerland : Springer, 2019. p. 59-70 (Lecture Notes in Computer Science ; Vol. 11625 ).

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

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AU - Yang, Jie

AU - Gasevic, Dragan

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N2 - Automatic question generation, which aims at converting sentences in an article to high-quality questions, is an important task for educational practices. Recent work mainly focuses on designing effective generation architectures based on deep neural networks. However, the first and possibly the foremost step of automatic question generation has largely been ignored, i.e., identifying sentences carrying important information or knowledge that is worth asking questions about. In this work, we (i) propose a total of 9 strategies, which are grounded on heuristic question-asking assumptions, to determine sentences that are question-worthy, and (ii) compare their performance on 4 datasets by using the identified sentences as input for a well-trained question generator. Through extensive experiments, we show that (i) LexRank, a stochastic graph-based method for selecting important sentences from articles, gives robust performance across all datasets, (ii) questions collected in educational settings feature a more diverse set of source sentences than those obtained in non-educational settings, and (iii) more research efforts are needed to further improve the design of educational question generation architectures.

AB - Automatic question generation, which aims at converting sentences in an article to high-quality questions, is an important task for educational practices. Recent work mainly focuses on designing effective generation architectures based on deep neural networks. However, the first and possibly the foremost step of automatic question generation has largely been ignored, i.e., identifying sentences carrying important information or knowledge that is worth asking questions about. In this work, we (i) propose a total of 9 strategies, which are grounded on heuristic question-asking assumptions, to determine sentences that are question-worthy, and (ii) compare their performance on 4 datasets by using the identified sentences as input for a well-trained question generator. Through extensive experiments, we show that (i) LexRank, a stochastic graph-based method for selecting important sentences from articles, gives robust performance across all datasets, (ii) questions collected in educational settings feature a more diverse set of source sentences than those obtained in non-educational settings, and (iii) more research efforts are needed to further improve the design of educational question generation architectures.

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PB - Springer

CY - Cham Switzerland

ER -

Chen G, Yang J, Gasevic D. A comparative study on question-worthy sentence selection strategies for educational question generation. In Isotani S, Millán E, Ogan A, Hastings P, McLaren B, Luckin R, editors, Artificial Intelligence in Education : 20th International Conference, AIED 2019 Chicago, IL, USA, June 25–29, 2019 Proceedings, Part I. Cham Switzerland: Springer. 2019. p. 59-70. (Lecture Notes in Computer Science ). https://doi.org/10.1007/978-3-030-23204-7_6