VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups (2106.00676v3)

Abstract: Accurately extracting structured content from PDFs is a critical first step for NLP over scientific papers. Recent work has improved extraction accuracy by incorporating elementary layout information, e.g., each token's 2D position on the page, into LLM pretraining. We introduce new methods that explicitly model VIsual LAyout (VILA) groups, i.e., text lines or text blocks, to further improve performance. In our I-VILA approach, we show that simply inserting special tokens denoting layout group boundaries into model inputs can lead to a 1.9% Macro F1 improvement in token classification. In the H-VILA approach, we show that hierarchical encoding of layout-groups can result in up-to 47% inference time reduction with less than 0.8% Macro F1 loss. Unlike prior layout-aware approaches, our methods do not require expensive additional pretraining, only fine-tuning, which we show can reduce training cost by up to 95%. Experiments are conducted on a newly curated evaluation suite, S2-VLUE, that unifies existing automatically-labeled datasets and includes a new dataset of manual annotations covering diverse papers from 19 scientific disciplines. Pre-trained weights, benchmark datasets, and source code are available at https://github.com/allenai/VILA.

• The paper introduces novel I-VILA and H-VILA methods that improve structured extraction, boosting Macro F1 by 1.9% and reducing inference time by up to 47%.
• The paper demonstrates that integrating visual layout tokens cuts pretraining needs by up to 95%, streamlining deployment and reducing computational costs.
• The paper validates these approaches using the S2-VLUE benchmark suite across 19 disciplines, ensuring a robust evaluation of document processing efficiency.

Structured Content Extraction from Scientific PDFs: A VILA Approach

The paper "VILA: Improving Structured Content Extraction from Scientific PDFs Using Visual Layout Groups" addresses the significant challenge of accurately extracting structured information from scientific PDF documents. PDFs are a prevalent format for furnishing scientific literature, yet they lack the semantic markup needed for immediate downstream NLP tasks. Thus, effectively deciphering their structure is crucial for facilitating deep understanding and accessibility of scientific content.

Research Innovations

The authors present two novel methodologies, I-VILA and H-VILA, which leverage Visual Layout (VILA) groups—specifically, text lines and blocks—within scientific documents to enhance the extraction process.

1. I-VILA: This approach inserts special tokens into models to denote layout group boundaries. Such tokens act as indicators for potential semantic changes in text content. The inclusion of these indicators into LLM inputs has resulted in a significant improvement of 1.9% in Macro F1 score for token classification tasks. Interestingly, I-VILA stands out since it accomplishes these improvements without necessitating extra pretraining—simplifying deployment and reducing computational costs by up to 95%.
2. H-VILA: Here, a hierarchical model architecture is used where text groups are encoded separately and modeled collectively at the page level. These layout group structures substantially reduce inference time by up to 47%, with only a minor 0.8% loss in Macro F1 performance. H-VILA also emphasizes the efficiency gains that layout-awareness can introduce, challenging existing state-of-the-art models, which typically rely on large computational resources for pretraining.

Datasets and Evaluation

Critical to the paper's methodologies is the introduction of the S2-VLUE benchmark suite, which consolidates existing datasets and introduces a new, manually annotated set called S2-VL. S2-VL encompasses papers from 19 scientific disciplines, providing a diverse and comprehensive evaluation suite. This contributes significantly to advancing model generalization across varied layouts and ensuring robustness against diverse document structures.

Implications and Future Directions

The implications of this research are twofold:

• Practical: In applications requiring efficient and accurate PDF content extraction, VILA methods allow for significant resource savings while maintaining or improving upon the accuracy of document processing tasks. These approaches align with the goals of green AI, prioritizing efficiency alongside effectiveness.
• Theoretical: By explicitly encoding layout information, the paper contributes to the broader understanding of integrating spatial and structural data within NLP models. This opens pathways to novel architectures that could harness multimodal inputs (textual, visual, spatial) for improved performance.

Conclusion

The paper demonstrates that careful modeling of document layouts through VILA groups can achieve notable improvements in structured content extraction from scientific PDFs without heavy computational resources. The research emphasizes that the integration of structural tokens and hierarchical models can effectively enhance LLMs' comprehension of complex document layouts. Going forward, advancements and fine-tuning in VILA group detection methods stand to further bridge existing gaps in document processing, and similar strategies could translate effectively across varying types of document-heavy domains.