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        <title>ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent
%A Kim, Hyun
%A Cho, Minsoo
%A Na, Seung-Hoon
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F kim-etal-2023-explainmeetsum
%X 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.
%R 10.18653/v1/2023.acl-long.731
%U https://aclanthology.org/2023.acl-long.731
%U https://doi.org/10.18653/v1/2023.acl-long.731
%P 13079-13098

Markdown (Informal)

[ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent](https://aclanthology.org/2023.acl-long.731) (Kim et al., ACL 2023)

• ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent (Kim et al., ACL 2023)

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.