README.md

---
license: cc-by-nc-sa-4.0
language: ja
tags:
- advertisement
task_categories:
- text2text-generation
- image-to-text
size_categories: 10K<n<100K
pretty_name: camera
dataset_info:
- config_name: with-lp-images
  features:
  - name: asset_id
    dtype: int64
  - name: kw
    dtype: string
  - name: lp_meta_description
    dtype: string
  - name: title_org
    dtype: string
  - name: title_ne1
    dtype: string
  - name: title_ne2
    dtype: string
  - name: title_ne3
    dtype: string
  - name: domain
    dtype: string
  - name: parsed_full_text_annotation
    sequence:
    - name: text
      dtype: string
    - name: xmax
      dtype: int64
    - name: xmin
      dtype: int64
    - name: ymax
      dtype: int64
    - name: ymin
      dtype: int64
  - name: lp_image
    dtype: image
  splits:
  - name: test
    num_bytes: 2528981570
    num_examples: 872
  - name: validation
    num_bytes: 13133740369.43
    num_examples: 3098
  - name: train
    num_bytes: 51367983297.415
    num_examples: 12395
  download_size: 65867475365
  dataset_size: 67030705236.845
- config_name: without-lp-images
  features:
  - name: asset_id
    dtype: int64
  - name: kw
    dtype: string
  - name: lp_meta_description
    dtype: string
  - name: title_org
    dtype: string
  - name: title_ne1
    dtype: string
  - name: title_ne2
    dtype: string
  - name: title_ne3
    dtype: string
  - name: domain
    dtype: string
  - name: parsed_full_text_annotation
    sequence:
    - name: text
      dtype: string
    - name: xmax
      dtype: int64
    - name: xmin
      dtype: int64
    - name: ymax
      dtype: int64
    - name: ymin
      dtype: int64
  splits:
  - name: test
    num_bytes: 14634833
    num_examples: 872
  - name: validation
    num_bytes: 69170878
    num_examples: 3098
  - name: train
    num_bytes: 280633510
    num_examples: 12395
  download_size: 150489014
  dataset_size: 364439221
configs:
- config_name: with-lp-images
  data_files:
  - split: test
    path: with-lp-images/test-*
  - split: validation
    path: with-lp-images/validation-*
  - split: train
    path: with-lp-images/train-*
  default: true
- config_name: without-lp-images
  data_files:
  - split: test
    path: without-lp-images/test-*
  - split: validation
    path: without-lp-images/validation-*
  - split: train
    path: without-lp-images/train-*

---

# Dataset Card for CAMERA📷:

## Table of Contents:
- [Dataset Card for Camera](#dataset-card-for-camera)
  - [Table of Contents](#table-of-contents)
  - [Dataset Details](#dataset-details)
    - [Dataset Description](#dataset-description)
    - [Dataset Sources](#dataset-sources)
  - [Uses](#uses)
    - [Direct Use](#direct-use)
    - [Dataset Information](#datasest-information)
    - [Data Example](#data-example)
    - [Dataset Structure](#dataset-structure)
  - [Citation](#citation)


## Dataset Details

### Dataset Description
CAMERA (CyberAgent Multimodal Evaluation for Ad Text GeneRAtion) is the Japanese ad text generation dataset, which comprises actual data sourced from Japanese search ads and incorporates annotations encompassing multi-modal information such as the LP images.

### Dataset Sources

- **Homepage:** [Github](https://github.com/CyberAgentAILab/camera)
- **Paper:** [Striking Gold in Advertising: Standardization and Exploration of Ad Text
Generation](https://aclanthology.org/2024.acl-long.54/)
  - [NEW!] Our paper has been accepted to [ACL2024](https://2024.aclweb.org/), and we will update the paper information as soon as the proceedings are published.

## Uses

### Direct Use

- Dataset with lp images (with-lp-images)
```python
import datasets
dataset = datasets.load_dataset("cyberagent/camera", name="with-lp-images")
```

- Dataset without lp images (without-lp-images)
```python
import datasets
dataset = datasets.load_dataset("cyberagent/camera", name="without-lp-images")
```

### Dataset Information

- with-lp-images
```
DatasetDict({
    train: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
        num_rows: 12395
    })
    validation: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
        num_rows: 3098
    })
    test: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation', 'lp_image'],
        num_rows: 872
    })
})
```

- without-lp-images
```
DatasetDict({
    train: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
        num_rows: 12395
    })
    validation: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
        num_rows: 3098
    })
    test: Dataset({
        features: ['asset_id', 'kw', 'lp_meta_description', 'title_org', 'title_ne1', 'title_ne2', 'title_ne3', 'domain', 'parsed_full_text_annotation'],
        num_rows: 872
    })
})
```

### Data Example

```
{'asset_id': 6041,
 'kw': 'GLLARE MARUYAMA',
 'lp_meta_description': '美容サロン ブルーヘアー 札幌市 西区 琴似 創業34年 かゆみ、かぶれを防ぎ、美しい髪へ',
 'title_org': '北海道、水の教会で結婚式',
 'title_ne1': '',
 'title_ne2': '',
 'title_ne3': '',
 'domain': '',
 'parsed_full_text_annotation': {
  'text': ['表参道',
   '名古屋',
   '梅田',
    ...
   '成約者様専用ページ',
   '個人情報保護方針',
   '星野リゾートトマム'],
  'xmax': [163,
   162,
   157,
    ...
   1047,
   1035,
   1138],
  'xmin': [125,
   125,
   129,
    ...
   937,
   936,
   1027],
  'ymax': [9652,
   9791,
   9928,
    ...
   17119,
   17154,
   17515],
  'ymin': [9642,
   9781,
   9918,
    ...
   17110,
   17143,
   17458]},
 'lp_image': <PIL.PngImagePlugin.PngImageFile image mode=RGBA size=1200x17596>}
```

### Dataset Structure

| Name | Description |
| ---- | ---- |
| asset_id | ids (associated with LP images) |
| kw | search keyword |
| lp_meta_description | meta description extracted from LP (i.e., LP Text)|
| title_org  |  ad text (original gold reference) |
| title_ne{1-3}  |  ad text (additonal gold references for multi-reference evaluation |
| domain |  industry domain (HR, EC, Fin, Edu) for industry-wise evaluation |
| parsed_full_text_annotation | OCR result for LP image |
| lp_image | LP image |

## Citation

```
@inproceedings{mita-etal-2024-striking,
    title = "Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation",
    author = "Mita, Masato  and
      Murakami, Soichiro  and
      Kato, Akihiko  and
      Zhang, Peinan",
    editor = "Ku, Lun-Wei  and
      Martins, Andre  and
      Srikumar, Vivek",
    booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = aug,
    year = "2024",
    address = "Bangkok, Thailand and virtual meeting",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.acl-long.54",
    pages = "955--972",
    abstract = "In response to the limitations of manual ad creation, significant research has been conducted in the field of automatic ad text generation (ATG). However, the lack of comprehensive benchmarks and well-defined problem sets has made comparing different methods challenging. To tackle these challenges, we standardize the task of ATG and propose a first benchmark dataset, CAMERA, carefully designed and enabling the utilization of multi-modal information and facilitating industry-wise evaluations. Our extensive experiments with a variety of nine baselines, from classical methods to state-of-the-art models including large language models (LLMs), show the current state and the remaining challenges. We also explore how existing metrics in ATG and an LLM-based evaluator align with human evaluations.",
}
```