@inproceedings{mascarell-etal-2024-information,
    title = "Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition",
    author = "Mascarell, Laura  and
      LHomme, Yan  and
      El Helou, Majed",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2024.findings-acl.316/",
    doi = "10.18653/v1/2024.findings-acl.316",
    pages = "5333--5338",
    abstract = "Understanding the nature of high-quality summaries is crucial to further improve the performance of multi-document summarization. We propose an approach to characterize human-written summaries using partial information decomposition, which decomposes the mutual information provided by all source documents into union, redundancy, synergy, and unique information. Our empirical analysis on different MDS datasets shows that there is a direct dependency between the number of sources and their contribution to the summary."
}