metadata

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: dev
        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: dev
        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: dev
        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: dev
        path: without-lp-images/validation-*
      - split: train
        path: without-lp-images/train-*

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
    })
    dev: 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
    })
    dev: 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.",
}

Datasets:

cyberagent
/

camera