LOCOST: State-space Models For Long Document Abstractive Summarization

Bronnec Florian Le, Duong Song, Ravaut Mathieu, Allauzen Alexandre, Chen Nancy F., Guigue Vincent, Lumbreras Alberto, Soulier Laure, Gallinari Patrick. Arxiv 2024

[Paper]    
Applications Attention Mechanism Language Modeling Model Architecture Pretraining Methods Training Techniques Transformer

State-space models are a low-complexity alternative to transformers for encoding long sequences and capturing long-term dependencies. We propose LOCOST: an encoder-decoder architecture based on state-space models for conditional text generation with long context inputs. With a computational complexity of \(O(L log L)\), this architecture can handle significantly longer sequences than state-of-the-art models that are based on sparse attention patterns. We evaluate our model on a series of long document abstractive summarization tasks. The model reaches a performance level that is 93-96% comparable to the top-performing sparse transformers of the same size while saving up to 50% memory during training and up to 87% during inference. Additionally, LOCOST effectively handles input texts exceeding 600K tokens at inference time, setting new state-of-the-art results on full-book summarization and opening new perspectives for long input processing.

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