@inproceedings{kim-etal-2023-explainmeetsum,
    title = "{E}xplain{M}eet{S}um: A Dataset for Explainable Meeting Summarization Aligned with Human Intent",
    author = "Kim, Hyun  and
      Cho, Minsoo  and
      Na, Seung-Hoon",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.acl-long.731",
    doi = "10.18653/v1/2023.acl-long.731",
    pages = "13079--13098",
    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.",
}

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.
Kim, Hyun Cho, Minsoo Na, Seung-Hoon
ExplainMeetSum: A Dataset for Explainable Meeting Summarization Aligned with Human Intent