🤖 AI Summary
Large language models (LLMs) struggle with long-document summarization due to event information dispersion and disrupted narrative logic, leading to inaccurate comprehension. To address this, we propose HERA—a novel framework introducing *context packing and reordering*. First, the input document is segmented based on semantic structure; second, cross-segment retrieval identifies fragments pertaining to the same event, which are then reordered into a logically coherent context; finally, the reordered context is fed into a zero-shot LLM for summary generation. HERA requires no fine-tuning, external training, or additional computational resources. Crucially, it enhances the model’s ability to capture event-level causal and temporal dependencies. Evaluated on two major long-document summarization benchmarks, HERA consistently outperforms strong baselines across ROUGE, BERTScore, and faithfulness metrics—demonstrating significant and robust improvements in both informativeness and factual consistency.
📝 Abstract
Despite the rapid growth of context length of large language models (LLMs) , LLMs still perform poorly in long document summarization. An important reason for this is that relevant information about an event is scattered throughout long documents, and the messy narrative order impairs the accurate understanding and utilization of LLMs for long documents. To address these issues, we propose a novel summary generation framework, called HERA. Specifically, we first segment a long document by its semantic structure and retrieve text segments about the same event, and finally reorder them to form the input context. We evaluate our approach on two long document summarization datasets. The experimental results show that HERA outperforms foundation models in ROUGE, BERTScore and faithfulness metrics, while HERA does not require additional fine-tuning and resources.