Which Information Matters? Dissecting Human-written Multi-document Summaries with Partial Information Decomposition
Author / Producer
Date
2024-08
Publication Type
Conference Paper
ETH Bibliography
yes
Citations
Altmetric
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Rights / License
Abstract
Understanding the nature of high-quality sum maries is crucial to further improve the perfor mance 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 in formation. Our empirical analysis on different MDS datasets shows that there is a direct de pendency between the number of sources and their contribution to the summary.
Permanent link
Publication status
published
Book title
Findings of the Association for Computational Linguistics: ACL 2024
Journal / series
Volume
Pages / Article No.
5333 - 5338
Publisher
Association for Computational Linguistics
Event
62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
Organisational unit
02154 - Media Technology Center (MTC) / Media Technology Center (MTC)