Dataset Card for CAMERA📷:

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
Paper: Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation
- [NEW!] Our paper has been accepted to ACL2024, and we will update the paper information as soon as the proceedings are published.

Uses

Direct Use

Dataset with lp images (with-lp-images)

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

Dataset without lp images (without-lp-images)

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

@misc{mita2024striking,
      title={Striking Gold in Advertising: Standardization and Exploration of Ad Text Generation},
      author={Masato Mita and Soichiro Murakami and Akihiko Kato and Peinan Zhang},
      year={2024},
      eprint={2309.12030},
      archivePrefix={arXiv},
      primaryClass={id='cs.CL' full_name='Computation and Language' is_active=True alt_name='cmp-lg' in_archive='cs' is_general=False description='Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.'}
}

Datasets:

cyberagent
/

camera