Abstract
To enhance the explainability of meeting summarization, we construct a new dataset called “ExplainMeetSum,” an augmented version of QMSum, by newly annotating evidence sentences that faithfully “explain” a summary. Using ExplainMeetSum, we propose a novel multiple extractor guided summarization, namely Multi-DYLE, which extensively generalizes DYLE to enable using a supervised extractor based on human-aligned extractive oracles. We further present an explainability-aware task, named “Explainable Evidence Extraction” (E3), which aims to automatically detect all evidence sentences that support a given summary. Experimental results on the QMSum dataset show that the proposed Multi-DYLE outperforms DYLE with gains of up to 3.13 in the ROUGE-1 score. We further present the initial results on the E3 task, under the settings using separate and joint evaluation metrics.- Anthology ID:
- 2023.acl-long.731
- Volume:
- Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- July
- Year:
- 2023
- Address:
- Toronto, Canada
- Editors:
- Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 13079–13098
- Language:
- URL:
- https://aclanthology.org/2023.acl-long.731
- DOI:
- 10.18653/v1/2023.acl-long.731
- Cite (ACL):
- Hyun Kim, Minsoo Cho, and Seung-Hoon Na. 2023. ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 13079–13098, Toronto, Canada. Association for Computational Linguistics.
- Cite (Informal):
- ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (Kim et al., ACL 2023)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2023.acl-long.731.pdf