Analyzing Interpretability of Summarization Model with Eye-gaze Information

Fariz Ikhwantri, Hiroaki Yamada, Takenobu Tokunaga

Abstract
Interpretation methods provide saliency scores indicating the importance of input words for neural summarization models. Prior work has analyzed models by comparing them to human behavior, often using eye-gaze as a proxy for human attention in reading tasks such as classification. This paper presents a framework to analyze the model behavior in summarization by comparing it to human summarization behavior using eye-gaze data. We examine two research questions: RQ1) whether model saliency conforms to human gaze during summarization and RQ2) how model saliency and human gaze affect summarization performance. For RQ1, we measure conformity by calculating the correlation between model saliency and human fixation counts. For RQ2, we conduct ablation experiments removing words/sentences considered important by models or humans. Experiments on two datasets with human eye-gaze during summarization partially confirm that model saliency aligns with human gaze (RQ1). However, ablation experiments show that removing highly-attended words/sentences from the human gaze does not significantly degrade performance compared with the removal by the model saliency (RQ2).
Anthology ID:
2024.lrec-main.84
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
939–950
Language:
URL:
https://aclanthology.org/2024.lrec-main.84
DOI:
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Cite (ACL):
Fariz Ikhwantri, Hiroaki Yamada, and Takenobu Tokunaga. 2024. Analyzing Interpretability of Summarization Model with Eye-gaze Information. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 939–950, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Analyzing Interpretability of Summarization Model with Eye-gaze Information (Ikhwantri et al., LREC-COLING 2024)
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