@inproceedings{t-revi-etal-2024-rationale-based,
title = "Rationale-based Learning Using Self-Supervised Narrative Events for Text Summarisation of Interactive Digital Narratives",
author = "T Revi, Ashwathy and
Middleton, Stuart E. and
Millard, David E.",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.1186",
pages = "13557--13585",
abstract = "This paper explores using rationale-based learning with supervised attention to focus the training of text summarisation models on words and sentences surrounding choice points for Interactive Digital Narratives (IDNs). IDNs allow players to interact with the story via choice points, making choices central to these narratives. Exploiting such knowledge about narrative structure during model training can help ensure key narrative information appears in generated summaries of narrative-based text and thus improve the quality of these summaries. We experiment with using word-level and sentence-level rationales indicating the proximity of words and sentences to self-supervised choice points. Our results indicate that rationale-based learning can improve the ability of attention-based text summarisation models to create higher quality summaries that encode key narrative information better for different playthroughs of the same interactive narrative. These results suggest a promising new direction for narrative-based text summarisation models.",
}
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<abstract>This paper explores using rationale-based learning with supervised attention to focus the training of text summarisation models on words and sentences surrounding choice points for Interactive Digital Narratives (IDNs). IDNs allow players to interact with the story via choice points, making choices central to these narratives. Exploiting such knowledge about narrative structure during model training can help ensure key narrative information appears in generated summaries of narrative-based text and thus improve the quality of these summaries. We experiment with using word-level and sentence-level rationales indicating the proximity of words and sentences to self-supervised choice points. Our results indicate that rationale-based learning can improve the ability of attention-based text summarisation models to create higher quality summaries that encode key narrative information better for different playthroughs of the same interactive narrative. These results suggest a promising new direction for narrative-based text summarisation models.</abstract>
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%0 Conference Proceedings
%T Rationale-based Learning Using Self-Supervised Narrative Events for Text Summarisation of Interactive Digital Narratives
%A T Revi, Ashwathy
%A Middleton, Stuart E.
%A Millard, David E.
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F t-revi-etal-2024-rationale-based
%X This paper explores using rationale-based learning with supervised attention to focus the training of text summarisation models on words and sentences surrounding choice points for Interactive Digital Narratives (IDNs). IDNs allow players to interact with the story via choice points, making choices central to these narratives. Exploiting such knowledge about narrative structure during model training can help ensure key narrative information appears in generated summaries of narrative-based text and thus improve the quality of these summaries. We experiment with using word-level and sentence-level rationales indicating the proximity of words and sentences to self-supervised choice points. Our results indicate that rationale-based learning can improve the ability of attention-based text summarisation models to create higher quality summaries that encode key narrative information better for different playthroughs of the same interactive narrative. These results suggest a promising new direction for narrative-based text summarisation models.
%U https://aclanthology.org/2024.lrec-main.1186
%P 13557-13585
Markdown (Informal)
[Rationale-based Learning Using Self-Supervised Narrative Events for Text Summarisation of Interactive Digital Narratives](https://aclanthology.org/2024.lrec-main.1186) (T Revi et al., LREC-COLING 2024)
ACL