Extract Paper Digest — Research AI

Researchers analyze thousands of documents for insights. Manual analysis is time-consuming and may miss connections.

16
Fields Extracted
300s
Max Processing
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What This Template Does

AI-powered extraction using gemini-2.5-flash. Part of 113 production-ready templates.

Capabilities

  • Data Extraction
  • Summarization
  • Document Processing
  • Academic
  • Papers

Output Schema

{
  "$schema": "http://json-schema.org/draft-07/schema#",
  "title": "Paper Digest Output Schema",
  "description": "Schema for academic paper digest extraction output",
  "type": "object",
  "properties": {
    "title": {
      "type": "string",
      "description": "The exact title of the academic paper"
    },
    "authors": {
      "type": "array",
      "items": {
        "type": "string"
      },
      "description": "List of all authors in publication order"
    },
    "publication_date":
...

Quick Start

$ pip install doclayer
$ doclayer process document.pdf --agent research.paper-digest

See It In Action

Real extraction example showing input document and structured output.

Input Document
Neural Architecture Search for Efficient Vision Transformers

Authors: Elena Rodriguez, Michael Chen, Sarah Patel, James Williams

Published: Proceedings of the 41st International Conference on Machine Learning (ICML 2024)
Date: July 21-27, 2024
Location: Vienna, Austria

DOI: 10.1109/ICML.2024.00847

Abstract:
Vision Transformers (ViTs) have achieved remarkable success in computer vision tasks but often require substantial computational resources. We present NAS-ViT, a neural architecture searc
Extracted Data
{
  "title": "Neural Architecture Search for Efficient Vision Transformers",
  "authors": [
    "Elena Rodriguez",
    "Michael Chen",
    "Sarah Patel",
    "James Williams"
  ],
  "publication_date": "2024-07-21",
  "venue": "Proceedings of the 41st International Conference on Machine Learning (ICML 2024)",
  "venue_type": "conference",
  "doi": "10.1109/ICML.2024.00847",
  "abstract": "Presents NAS-ViT, a neural architecture search framework for discovering efficient vision transformer architectures using differentiable search with hardware-aware constraints.",
  "methodology": [
    "Differentiable neural architecture search",
    "Hardware-aware search constraints",
    "Variable patch size mechanism",
    "Attention head configuration optimization",
    "Depth-width trade-off exploration"
  ],
  "results": "94.2% top-1 accuracy on ImageNet with 4.8 GFLOPs (2.3x efficiency improvement over DeiT-Base); 52.1 mAP on COCO object detection; 48.7 mIoU on ADE20K segmentation",
  "impact": "Demonstrates that neural architecture search can significantly reduce computational requirements for vision transformers while maintaining high accuracy, with transferable benefits across multiple vision tasks",
  "citation_count": 127,
  "key_contributions": [
    "Novel search space including variable patch sizes and attention configurations",
    "Hardware-aware differentiable search algorithm",
    "2.3x efficiency improvement over baseline ViT models",
    "Demonstrated transferability to detection and segmentation tasks",
    "Ablation studies identifying key efficiency mechanisms"
  ],
  "keywords": [
    "Neural Architecture Search",
    "Vision Transformers",
    "Efficient Deep Learning",
    "Computer Vision"
  ],
  "research_field": "Computer Vision / Machine Learning",
  "document_type": "conference_paper"
}

Example demonstrating extraction of key findings, methodology, and results from academic paper. Produces concise abstract with main contributions, research questions answered, and implications for the field.

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Frequently Asked Questions

What documents can Paper Digest process?

The Paper Digest template processes research documents including various formats and layouts. See the instructions for specific document types supported.

How accurate is the Paper Digest extraction?

The Paper Digest template uses Gemini 2.5 Flash for high-accuracy extraction. Results include confidence scores for each field.

Can I customize the Paper Digest template?

Yes, you can modify the extraction schema, add custom fields, or adjust the instructions to match your specific requirements.

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