I’m Training a Transformer Model for Text Summarization, but It Struggles with Long Documents - How Can I Improve Its Performance? #6454
-
|
I’m working on a text summarization project using a transformer-based model, but it performs poorly on documents longer than 1,000 tokens. The summaries tend to miss key points or become incoherent. I’m currently using a standard encoder-decoder architecture with a max token limit of 512. Would increasing the context window or switching to models like Longformer or GPT-4 with 32k token support help? Are there other strategies, such as hierarchical attention or chunking methods, that can improve summarization for long-form content? |
Beta Was this translation helpful? Give feedback.
Replies: 1 comment
-
|
Yes, the token limit is a common bottleneck in transformer models. Switching to architectures like Longformer, BigBird, or GPT-4 with a 32,000-token context window can significantly enhance performance on long documents. These models use sparse attention mechanisms to manage longer input sequences efficiently. Additionally, applying hierarchical models—where the document is split into sections, each summarized individually, and then combined—can preserve coherence and coverage. Chunking combined with positional embeddings or attention bridging between chunks also helps. Data preprocessing and fine-tuning with long-document datasets can further improve summarization quality. |
Beta Was this translation helpful? Give feedback.
Yes, the token limit is a common bottleneck in transformer models. Switching to architectures like Longformer, BigBird, or GPT-4 with a 32,000-token context window can significantly enhance performance on long documents. These models use sparse attention mechanisms to manage longer input sequences efficiently. Additionally, applying hierarchical models—where the document is split into sections, each summarized individually, and then combined—can preserve coherence and coverage. Chunking combined with positional embeddings or attention bridging between chunks also helps. Data preprocessing and fine-tuning with long-document datasets can further improve summarization quality.