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Conference Paper
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent
- Authors
- Hyun Kim, Minsoo Cho, Seung-Hoon Na
- Issue Date
- 2023-07
- Citation
- Annual Meeting of the Association for Computational Linguistics (ACL) 2023, pp.13079-13098
- Language
- English
- Type
- Conference Paper
- 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.
- KSP Keywords
- Meeting Summarization, evaluation metrics
This work is distributed under the term of Creative Commons License (CCL)
(CC BY)
(CC BY)
