README.md

---
license: cc-by-4.0
language:
- en
- zh
viewer: true
configs:
- config_name: default
  data_files:
  - split: val
    path: data/**/*.jsonl
---
# ScienceMetaBench

[English](README.md) | [中文](README_ZH.md)

🤗 [HuggingFace Dataset](https://huggingface.co/datasets/opendatalab/ScienceMetaBench) | 🔍 [Dingo](https://github.com/MigoXLab/dingo)

ScienceMetaBench is a benchmark dataset for evaluating the accuracy of metadata extraction from scientific literature PDF files. The dataset covers three major categories: academic papers, textbooks, and ebooks, and can be used to assess the performance of Vision Language Models (VLMs) or other information extraction systems.

## 📊 Dataset Overview

### Data Types

This benchmark includes three types of scientific literature:

1. **Papers**
   - Mainly from academic journals and conferences
   - Contains academic metadata such as DOI, keywords, etc.

2. **Textbooks**
   - Formally published textbooks
   - Includes ISBN, publisher, and other publication information

3. **Ebooks**
   - Digitized historical documents and books
   - Covers multiple languages and disciplines

### Data Batches

This benchmark has undergone two rounds of data expansion, with each round adding new sample data:

```
data/
├── 20250806/          # First batch (August 6, 2024)
│   ├── ebook_0806.jsonl
│   ├── paper_0806.jsonl
│   └── textbook_0806.jsonl
└── 20251022/          # Second batch (October 22, 2024)
    ├── ebook_1022.jsonl
    ├── paper_1022.jsonl
    └── textbook_1022.jsonl
```

**Note**: The two batches of data complement each other to form a complete benchmark dataset. You can choose to use a single batch or merge them as needed.

### PDF Files

The `pdf/` directory contains the original PDF files corresponding to the benchmark data, with a directory structure consistent with the `data/` directory.

**File Naming Convention**: All PDF files are named using their SHA256 hash values, in the format `{sha256}.pdf`. This naming scheme ensures file uniqueness and traceability, making it easy to locate the corresponding source file using the `sha256` field in the JSONL data.

## 📝 Data Format

All data files are in JSONL format (one JSON object per line).

### Academic Paper Fields

```json
{
  "sha256": "SHA256 hash of the file",
  "doi": "Digital Object Identifier",
  "title": "Paper title",
  "author": "Author name",
  "keyword": "Keywords (comma-separated)",
  "abstract": "Abstract content",
  "pub_time": "Publication year"
}
```

### Textbook/Ebook Fields

```json
{
  "sha256": "SHA256 hash of the file",
  "isbn": "International Standard Book Number",
  "title": "Book title",
  "author": "Author name",
  "abstract": "Introduction/abstract",
  "category": "Classification number (e.g., Chinese Library Classification)",
  "pub_time": "Publication year",
  "publisher": "Publisher"
}
```

## 📖 Data Examples

### Academic Paper Example

The following image shows an example of metadata fields extracted from an academic paper PDF:

![Academic Paper Example](images/paper_example.png)

As shown in the image, the following key information needs to be extracted from the paper's first page:
- **DOI**: Digital Object Identifier (e.g., `10.1186/s41038-017-0090-z`)
- **Title**: Paper title
- **Author**: Author name
- **Keyword**: List of keywords
- **Abstract**: Paper abstract
- **pub_time**: Publication time (usually the year)

### Textbook/Ebook Example

The following image shows an example of metadata fields extracted from the copyright page of a Chinese ebook PDF:

![Textbook Example](images/ebook_example.png)

As shown in the image, the following key information needs to be extracted from the book's copyright page:
- **ISBN**: International Standard Book Number (e.g., `978-7-5385-8594-0`)
- **Title**: Book title
- **Author**: Author/editor name
- **Publisher**: Publisher name
- **pub_time**: Publication time (year)
- **Category**: Book classification number
- **Abstract**: Content introduction (if available)

These examples demonstrate the core task of the benchmark test: accurately extracting structured metadata information from PDF documents in various formats and languages.

## 📊 Evaluation Metrics

### Core Evaluation Metrics

This benchmark uses a string similarity-based evaluation method, providing two core metrics:

### Similarity Calculation Rules

This benchmark uses a string similarity algorithm based on `SequenceMatcher`, with the following specific rules:

1. **Empty Value Handling**: One is empty and the other is not → similarity is 0
2. **Complete Match**: Both are identical (including both being empty) → similarity is 1
3. **Case Insensitive**: Convert to lowercase before comparison
4. **Sequence Matching**: Use longest common subsequence algorithm to calculate similarity (range: 0-1)

**Similarity Score Interpretation**:
- `1.0`: Perfect match
- `0.8-0.99`: Highly similar (may have minor formatting differences)
- `0.5-0.79`: Partial match (extracted main information but incomplete)
- `0.0-0.49`: Low similarity (extraction result differs significantly from ground truth)

#### 1. Field-level Accuracy

**Definition**: The average similarity score for each metadata field.

**Calculation Method**:
```
Field-level Accuracy = Σ(similarity of that field across all samples) / total number of samples
```

**Example**: Suppose evaluating the `title` field on 100 samples, the sum of title similarity for each sample divided by 100 gives the accuracy for that field.

**Use Cases**:
- Identify which fields the model performs well or poorly on
- Optimize extraction capabilities for specific fields
- For example: If `doi` accuracy is 0.95 and `abstract` accuracy is 0.75, the model needs improvement in extracting abstracts

#### 2. Overall Accuracy

**Definition**: The average of all evaluated field accuracies, reflecting the model's overall performance.

**Calculation Method**:
```
Overall Accuracy = Σ(field-level accuracies) / total number of fields
```

**Example**: Evaluating 7 fields (isbn, title, author, abstract, category, pub_time, publisher), sum these 7 field accuracies and divide by 7.

**Use Cases**:
- Provide a single quantitative metric for overall model performance
- Facilitate horizontal comparison between different models or methods
- Serve as an overall objective for model optimization

### Using the Evaluation Script

`compare.py` provides a convenient evaluation interface:

```python
from compare import main, write_similarity_data_to_excel

# Define file paths and fields to compare
file_llm = 'data/llm-label_textbook.jsonl'      # LLM extraction results
file_bench = 'data/benchmark_textbook.jsonl'     # Benchmark data

# For textbooks/ebooks
key_list = ['isbn', 'title', 'author', 'abstract', 'category', 'pub_time', 'publisher']

# For academic papers
# key_list = ['doi', 'title', 'author', 'keyword', 'abstract', 'pub_time']

# Run evaluation and get metrics
accuracy, key_accuracy, detail_data = main(file_llm, file_bench, key_list)

# Output results to Excel (optional)
write_similarity_data_to_excel(key_list, detail_data, "similarity_analysis.xlsx")

# View evaluation metrics
print("Field-level Accuracy:", key_accuracy)
print("Overall Accuracy:", accuracy)
```

### Output Files

The script generates an Excel file containing detailed sample-by-sample analysis:

- `sha256`: File identifier
- For each field (e.g., `title`):
  - `llm_title`: LLM extraction result
  - `benchmark_title`: Benchmark data
  - `similarity_title`: Similarity score (0-1)

## 📈 Statistics

### Data Scale

**First Batch (20250806)**:
- **Ebooks**: 70 records
- **Academic Papers**: 70 records
- **Textbooks**: 71 records
- **Subtotal**: 211 records

**Second Batch (20251022)**:
- **Ebooks**: 354 records
- **Academic Papers**: 399 records
- **Textbooks**: 46 records
- **Subtotal**: 799 records

**Total**: 1010 benchmark test records

The data covers multiple languages (English, Chinese, German, Greek, etc.) and multiple disciplines, with both batches together providing a rich and diverse set of test samples.

## 🎯 Application Scenarios

1. **LLM Performance Evaluation**: Assess the ability of large language models to extract metadata from PDFs
2. **Information Extraction System Testing**: Test the accuracy of OCR, document parsing, and other systems
3. **Model Fine-tuning**: Use as training or fine-tuning data to improve model information extraction capabilities
4. **Cross-lingual Capability Evaluation**: Evaluate the model's ability to process multilingual literature

## 🔬 Data Characteristics

- ✅ **Real Data**: Real metadata extracted from actual PDF files
- ✅ **Diversity**: Covers literature from different eras, languages, and disciplines
- ✅ **Challenging**: Includes ancient texts, non-English literature, complex layouts, and other difficult cases
- ✅ **Traceable**: Each record includes SHA256 hash and original path

## 📋 Dependencies

```python
pandas>=1.3.0
openpyxl>=3.0.0
```

Install dependencies:

```bash
pip install pandas openpyxl
```

## 🤝 Contributing

If you would like to:
- Report data errors
- Add new evaluation dimensions
- Expand the dataset

Please submit an Issue or Pull Request.

## 📧 Contact

If you have questions or suggestions, please contact us through Issues.

---

**Last Updated**: December 26, 2025