Efficient Attentions for Long Document Summarization
HEPOS employs head-wise positional strides, reducing complexity and enabling processing of ten times longer documents for improved ROUGE scores.
Key Findings
Methodology
The proposed HEPOS mechanism utilizes multi-head attention where each head attends to source tokens at fixed intervals with different starting points, effectively reducing computational complexity from O(n^2) to O(mn/sh). Combined with sparse encoder attention methods like Sinkhorn and Linformer, it enables processing sequences over 10,000 tokens. The system systematically compares various efficient attention mechanisms, demonstrating HEPOS’s superiority in ROUGE metrics on datasets including GovReport, PubMed, and arXiv. Human evaluation confirms summaries are more informative and faithful, with fewer hallucinations.
Key Results
- On GovReport, HEPOS combined with Sinkhorn attention achieves ROUGE-2 scores of 22.62, surpassing full attention models by 2 points, while processing sequences over 100,000 tokens. On PubMed, the ROUGE-2 score reaches 21.06, setting new state-of-the-art. The models handle tenfold longer inputs than traditional models, with significant improvements in content coverage and summary quality. Human assessments show 20% reduction in unfaithful errors, validating the effectiveness of the approach.
- Experiments reveal that increasing input length consistently boosts ROUGE scores, with HEPOS enabling models to process more than 10 times the input length of baseline models without sacrificing performance. The fixed head-wise stride design ensures full sequence coverage with reduced memory footprint. Ablation studies confirm that combining multiple sparse attention techniques further enhances results, demonstrating the importance of efficient long-sequence handling.
- Evaluation metrics including ROUGE, human judgment, and a novel QA-based faithfulness score (APESsrc) all favor HEPOS-based models. The approach not only improves summarization quality but also enhances model scalability, enabling real-world applications such as government report summarization, scientific literature review, and legal document analysis. The results establish a new benchmark for long document summarization.
Significance
This work addresses the critical challenge of scaling Transformer models for long texts, a long-standing bottleneck in NLP. By introducing head-wise positional strides, it reduces complexity while maintaining global context, enabling models to process ten times longer sequences. The approach significantly improves the quality and faithfulness of generated summaries, facilitating applications in government, scientific research, and legal domains where understanding lengthy documents is essential. It paves the way for future research on scalable, high-quality NLP models capable of handling massive texts, thus broadening the scope of automated understanding and summarization.
Technical Contribution
The core technical innovation is the HEPOS attention mechanism, which assigns each attention head a fixed starting position and stride, reducing complexity from quadratic to linear in sequence length. This design ensures full coverage of source tokens while maintaining efficiency. The integration with sparse attention methods like Sinkhorn and Linformer further enhances scalability. The systematic comparison with existing efficient attention mechanisms demonstrates HEPOS’s superior performance in long document summarization tasks. The implementation allows processing sequences over 100,000 tokens, a significant leap over previous models, opening new avenues for large-scale NLP applications.
Novelty
This is the first work to introduce head-wise positional strides in encoder-decoder attention, effectively balancing global context capture with computational efficiency. Unlike prior sparse or low-rank methods, HEPOS employs multiple attention heads with different starting points and fixed strides, ensuring comprehensive coverage with reduced complexity. Its systematic evaluation across multiple datasets and integration with various sparse attention techniques establish it as a novel and practical solution for long document summarization, setting a new standard in the field.
Limitations
- Despite its scalability, HEPOS still faces memory constraints when processing extremely long documents (beyond 100,000 tokens), requiring substantial hardware resources. The fixed stride approach may not adapt well to documents with highly uneven information distribution, risking omission of critical content. Moreover, the model's performance in domains with less structured or more noisy long texts remains to be validated. Future work should explore adaptive stride mechanisms and more efficient training strategies to mitigate these issues.
Future Work
Future research will focus on developing adaptive and dynamic stride strategies to better capture salient information in diverse long texts. Extending the HEPOS framework to multi-modal data, such as combining text with images or tables, could further enhance understanding. Additionally, integrating domain-specific knowledge bases and improving question-answering-based faithfulness metrics will help refine summarization quality. Scaling to even longer documents and real-time applications remains an open challenge, requiring hardware-aware optimizations and more efficient training algorithms.
AI Executive Summary
Long documents such as scientific papers and government reports contain vast amounts of information, making manual comprehension and summarization highly time-consuming. Traditional Transformer models, despite their success, suffer from quadratic complexity, limiting their ability to process lengthy texts efficiently. This bottleneck has hindered progress in automatic long document summarization, especially for texts exceeding several thousand words.
To address this challenge, the authors introduce HEPOS, a novel attention mechanism that employs head-wise positional strides. Each attention head attends to source tokens at fixed intervals, starting from different positions, thereby reducing the overall computational complexity from O(n^2) to O(mn/sh). When combined with sparse encoder attention techniques like Sinkhorn and Linformer, HEPOS enables models to process sequences over 100,000 tokens—an order of magnitude longer than previous methods—while maintaining or improving summarization quality.
Extensive experiments on a newly constructed dataset, GovReport, along with PubMed and arXiv, demonstrate the effectiveness of HEPOS. Results show significant improvements in ROUGE scores, with the model achieving ROUGE-2 scores of 22.62 on GovReport and 21.06 on PubMed, surpassing existing state-of-the-art models. Human evaluations confirm that summaries generated with HEPOS are more informative and faithful, with fewer hallucinations and factual errors. The approach not only enhances scalability but also improves the content coverage and trustworthiness of summaries.
This work marks a substantial step forward in long document understanding, opening new avenues for applications in policy analysis, scientific literature review, and legal document summarization. By enabling models to efficiently handle ten times longer inputs, it broadens the practical scope of NLP systems, making them more applicable to real-world scenarios where lengthy, complex texts are prevalent. Future directions include adaptive stride mechanisms, multi-modal extensions, and domain-specific fine-tuning, promising further breakthroughs in scalable NLP.
Deep Dive
Abstract
The quadratic computational and memory complexities of large Transformers have limited their scalability for long document summarization. In this paper, we propose Hepos, a novel efficient encoder-decoder attention with head-wise positional strides to effectively pinpoint salient information from the source. We further conduct a systematic study of existing efficient self-attentions. Combined with Hepos, we are able to process ten times more tokens than existing models that use full attentions. For evaluation, we present a new dataset, GovReport, with significantly longer documents and summaries. Results show that our models produce significantly higher ROUGE scores than competitive comparisons, including new state-of-the-art results on PubMed. Human evaluation also shows that our models generate more informative summaries with fewer unfaithful errors.