Scaling Multi-Document Event Summarization: Evaluating Compression vs. Full-Text Approaches
This work addresses the problem of efficiently summarizing large text collections for fields like journalism and research, but it is incremental as it evaluates existing methods without introducing new ones.
The study compared compression-based and full-text methods for multi-document summarization on datasets with about 100 documents per summary, finding that full-text and retrieval methods generally performed best, though compression methods showed promise at intermediate stages but suffered from information loss.
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.