Abstract
Summarizing long pieces of text is a principal task in natural language processing with Machine Learning-based text generation models such as Large Language Models (LLM) being particularly suited to it. Yet these models are often used as black-boxes, making them hard to interpret and debug. This has led to calls by practitioners and regulatory bodies to improve the explainability of such models as they find ever more practical use. In this survey, we present a dual-perspective review of the intersection between explainability and summarization by reviewing the current state of explainable text summarization and also highlighting how summarization techniques are effectively employed to improve explanations.- Anthology ID:
- 2024.inlg-main.49
- Volume:
- Proceedings of the 17th International Natural Language Generation Conference
- Month:
- September
- Year:
- 2024
- Address:
- Tokyo, Japan
- Editors:
- Saad Mahamood, Nguyen Le Minh, Daphne Ippolito
- Venue:
- INLG
- SIG:
- SIGGEN
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 631–645
- Language:
- URL:
- https://aclanthology.org/2024.inlg-main.49
- DOI:
- Cite (ACL):
- Mahdi Dhaini, Ege Erdogan, Smarth Bakshi, and Gjergji Kasneci. 2024. Explainability Meets Text Summarization: A Survey. In Proceedings of the 17th International Natural Language Generation Conference, pages 631–645, Tokyo, Japan. Association for Computational Linguistics.
- Cite (Informal):
- Explainability Meets Text Summarization: A Survey (Dhaini et al., INLG 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.inlg-main.49.pdf