Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches

• URL: http://arxiv.org/abs/2502.06617v1
• Date: Mon, 10 Feb 2025 16:15:08 GMT
• Title: Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
• Authors: Adithya Pratapa, Teruko Mitamura,
• Abstract summary: We contrast two classes of systems for large-scale multi-document summarization (MDS): compression and full-text. Full-text methods promise a lossless summary by relying on recent advances in long-context reasoning. We show that compression-based methods show strong promise at intermediate stages, even outperforming full-context.
• Score: 5.856976164399712
• License:
• Abstract: Automatically summarizing large text collections is a valuable tool for document research, with applications in journalism, academic research, legal work, and many other fields. In this work, we contrast two classes of systems for large-scale multi-document summarization (MDS): compression and full-text. Compression-based methods use a multi-stage pipeline and often lead to lossy summaries. Full-text methods promise a lossless summary by relying on recent advances in long-context reasoning. To understand their utility on large-scale MDS, we evaluated them on three datasets, each containing approximately one hundred documents per summary. Our experiments cover a diverse set of long-context transformers (Llama-3.1, Command-R, Jamba-1.5-Mini) and compression methods (retrieval-augmented, hierarchical, incremental). Overall, we find that full-text and retrieval methods perform the best in most settings. With further analysis into the salient information retention patterns, we show that compression-based methods show strong promise at intermediate stages, even outperforming full-context. However, they suffer information loss due to their multi-stage pipeline and lack of global context. Our results highlight the need to develop hybrid approaches that combine compression and full-text approaches for optimal performance on large-scale multi-document summarization.

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