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        <title>Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition</title>
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    <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.</abstract>
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%0 Conference Proceedings
%T Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition
%A Mascarell, Laura
%A LHomme, Yan
%A El Helou, Majed
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand and virtual meeting
%F mascarell-etal-2024-information
%X 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.
%U https://aclanthology.org/2024.findings-acl.316
%P 5333-5338

Markdown (Informal)

[Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition](https://aclanthology.org/2024.findings-acl.316) (Mascarell et al., Findings 2024)

• Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition (Mascarell et al., Findings 2024)

ACL

• Laura Mascarell, Yan LHomme, and Majed El Helou. 2024. Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition. In Findings of the Association for Computational Linguistics ACL 2024, pages 5333–5338, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.