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        <title>Investigating Efficiently Extending Transformers for Long Input Summarization</title>
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    <abstract>While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs still poses a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens, which achieves strong performance on long input summarization tasks comparable with much larger models.</abstract>
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%0 Conference Proceedings
%T Investigating Efficiently Extending Transformers for Long Input Summarization
%A Phang, Jason
%A Zhao, Yao
%A Liu, Peter
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F phang-etal-2023-investigating
%X While large pretrained Transformer models have proven highly capable at tackling natural language tasks, handling long sequence inputs still poses a significant challenge. One such task is long input summarization, where inputs are longer than the maximum input context of most models. Through an extensive set of experiments, we investigate what model architectural changes and pretraining paradigms most efficiently adapt a pretrained Transformer for long input summarization. We find that a staggered, block-local Transformer with global encoder tokens strikes a good balance of performance and efficiency, and that an additional pretraining phase on long sequences meaningfully improves downstream summarization performance. Based on our findings, we introduce PEGASUS-X, an extension of the PEGASUS model with additional long input pretraining to handle inputs of up to 16K tokens, which achieves strong performance on long input summarization tasks comparable with much larger models.
%R 10.18653/v1/2023.emnlp-main.240
%U https://aclanthology.org/2023.emnlp-main.240/
%U https://doi.org/10.18653/v1/2023.emnlp-main.240
%P 3946-3961

Markdown (Informal)

[Investigating Efficiently Extending Transformers for Long Input Summarization](https://aclanthology.org/2023.emnlp-main.240/) (Phang et al., EMNLP 2023)

• Investigating Efficiently Extending Transformers for Long Input Summarization (Phang et al., EMNLP 2023)

ACL

• Jason Phang, Yao Zhao, and Peter Liu. 2023. Investigating Efficiently Extending Transformers for Long Input Summarization. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 3946–3961, Singapore. Association for Computational Linguistics.