Abstract
| Original language | English |
|---|---|
| Title of host publication | Intelligent Tutoring Systems. |
| Subtitle of host publication | ITS 2018. |
| Editors | R. Nkambou, R. Azevedo, J. Vassileva |
| Publisher | Springer |
| Pages | 191-201 |
| Number of pages | 11 |
| DOIs | |
| Publication status | Published - 17 May 2018 |
| Event | Intelligent Tutoring Systems. ITS 2018. : Lecture Notes in Computer Science. - Montreal, Canada Duration: 11 Jun 2018 → 15 Jun 2018 Conference number: vol 10858 |
Conference
| Conference | Intelligent Tutoring Systems. ITS 2018. |
|---|---|
| Country | Canada |
| City | Montreal |
| Period | 11/06/18 → 15/06/18 |
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Scoring Summaries Using Recurrent Neural Networks. / Ruseti, Stefan; Dascalu, Mihai; Johnson, Amy M.; McNamara, Danielle S.; Balyan, Renu; McCarthy, Kathryn S.; Trausan-Matu, Stefan; Nkambou, Roger (Editor); Azevedo, Roger (Editor); Vassileva, Julita (Editor).
Intelligent Tutoring Systems.: ITS 2018.. ed. / R. Nkambou; R. Azevedo; J. Vassileva. Springer, 2018. p. 191-201.Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceeding › Academic › peer-review
TY - GEN
T1 - Scoring Summaries Using Recurrent Neural Networks
AU - Ruseti, Stefan
AU - Dascalu, Mihai
AU - Johnson, Amy M.
AU - McNamara, Danielle S.
AU - Balyan, Renu
AU - McCarthy, Kathryn S.
AU - Trausan-Matu, Stefan
A2 - Nkambou, Roger
A2 - Azevedo, Roger
A2 - Vassileva, Julita
A2 - Nkambou, R.
A2 - Azevedo, R.
A2 - Vassileva, J.
PY - 2018/5/17
Y1 - 2018/5/17
N2 - Summarization enhances comprehension and is considered an effective strategy to promote and enhance learning and deep understanding of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation requires a lot of effort and time. Although the need for automated support is stringent, there are only a few shallow systems available, most of which rely on basic word/n-gram overlaps. In this paper, we introduce a hybrid model that uses state-of-the-art recurrent neural networks and textual complexity indices to score summaries. Our best model achieves over 55% accuracy for a 3-way classification that measures the degree to which the main ideas from the original text are covered by the summary. Our experiments show that the writing style, represented by the textual complexity indices, together with the semantic content grasped within the summary are the best predictors, when combined. To the best of our knowledge, this is the first work of its kind that uses RNNs for scoring and evaluating summaries.
AB - Summarization enhances comprehension and is considered an effective strategy to promote and enhance learning and deep understanding of texts. However, summarization is seldom implemented by teachers in classrooms because the manual evaluation requires a lot of effort and time. Although the need for automated support is stringent, there are only a few shallow systems available, most of which rely on basic word/n-gram overlaps. In this paper, we introduce a hybrid model that uses state-of-the-art recurrent neural networks and textual complexity indices to score summaries. Our best model achieves over 55% accuracy for a 3-way classification that measures the degree to which the main ideas from the original text are covered by the summary. Our experiments show that the writing style, represented by the textual complexity indices, together with the semantic content grasped within the summary are the best predictors, when combined. To the best of our knowledge, this is the first work of its kind that uses RNNs for scoring and evaluating summaries.
U2 - 10.1007/978-3-319-91464-0_19
DO - 10.1007/978-3-319-91464-0_19
M3 - Conference article in proceeding
SP - 191
EP - 201
BT - Intelligent Tutoring Systems.
PB - Springer
ER -