README.md

metadata

license: cc-by-sa-3.0

The Wikipedia Webpage 2M (WikiWeb2M) Dataset

We present the WikiWeb2M dataset consisting of over 2 million English Wikipedia articles. Our released dataset includes all of the text content on each page, links to the images present, and structure metadata such as which section each text and image element comes from.

This dataset is a contribution from our paper A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding.

The dataset is stored as gzipped TFRecord files which can be downloaded here or on our GitHub repository.

WikiWeb2M Statistics

WikiWeb2M is the first multimodal open source dataset to include all page content in a unified format. Here we provide aggregate information about the WikiWeb2M dataset as well as the number of samples available with each of the fine-tuning tasks we design from it.

Number of	Train	Validation	Test
Pages	1,803,225	100,475	100,833
Sections	10,519,294	585,651	588,552
Unique Images	3,867,277	284,975	286,390
Total Images	5,340,708	299,057	300,666

Our data processing and filtering choices for each fine-tuning task are described in the paper.

Downstream Task Samples	Train	Validation	Test
Page Description Generation	1,435,263	80,103	80,339
Section Summarization	3,082,031	172,984	173,591
Contextual Image Captioning	2,222,814	124,703	124,188

Data and Task Examples

Here we illustrate how a single webpage can be processed into the three tasks we study: page description generation, section summarization, and contextual image captioning. The paper includes multiple Wikipedia article examples.

Usage

TFRecord Features

Here we provide the names of the fields included in the dataset, their tensorflow Sequence Example type, their data type, and a brief description.

Feature	Sequence Example Type	DType	Description
split	Context	string	Dataset split this page contributes to (e.g., train, val, or test)
page_url	Context	string	Wikipeda page URL
page_title	Context	string	Wikipedia page title, title of the article
raw_page_description	Context	string	Wikipedia page description, which is typically the same or very similar to the content of the first (root) section of the article
clean_page_description	Context	string	raw_page_description but with newline and tab characters removed; this provides the exact target text for our page description generation task
page_contains_images	Context	int64	Whether the Wikipedia page has images after our cleaning and processing steps
page_content_sections_without_table_list	Context	int64	Number of content sections with text or images that do not contain a list or table. This field can be used to reproduce data filtering for page description generation
is_page_description_sample	Context	int64	Whether a page is used as a sample for the page description fine-tuning task
section_title	Sequence	string	Titles of each section on the Wikipedia page, in order
section_index	Sequence	int64	Index of each section on the Wikipedia page, in order
section_depth	Sequence	int64	Depth of each section on the Wikipedia page, in order
section_heading_level	Sequence	int64	Heading level of each section on the Wikipedia page, in order
section_subsection_index	Sequence	int64	Subsection indices, grouped by section in order
section_parent_index	Sequence	int64	The parent section index of each section, in order
section_text	Sequence	string	The body text of each section, in order
is_section_summarization_sample	Sequence	int64	Whether a section is used as a sample for the section summarization fine-tuning task
section_raw_1st_sentence	Sequence	string	The processed out first sentence of each section, in order
section_clean_1st_sentence	Sequence	string	The same as section_raw_1st_sentence but with newline and tab characters removed. This provides the exact target text for our section summarization task
section_rest_sentence	Sequence	string	The processed out sentences following the first sentence of each section, in order
section_contains_table_or_list	Sequence	int64	Whether section content contains a table or list; this field is needed to be able to reproduce sample filtering for section summarization
section_contains_images	Sequence	int64	Whether each section has images after our cleaning and processing steps, in order
is_image_caption_sample	Sequence	int64	Whether an image is used as a sample for the image captioning fine-tuning task
section_image_url	Sequence	string	Image URLs, grouped by section in order
section_image_mime_type	Sequence	string	Image mime type, grouped by section in order
section_image_width	Sequence	int64	Image width, grouped by section in order
section_image_height	Sequence	int64	Image height, grouped by section in order
section_image_in_wit	Sequence	int64	Whether an image was originally contained in the WIT dataset, grouped by section in order
section_image_raw_attr_desc	Sequence	string	Image attribution description, grouped by section in order
section_image_clean_attr_desc	Sequence	string	The English only processed portions of the attribution description
section_image_raw_ref_desc	Sequence	string	Image reference description, grouped by section in order
section_image_clean_ref_desc	Sequence	string	The same as section_image_raw_ref_desc but with newline and tab characters removed; this provides the exact target text for our image captioning task
section_image_alt_text	Sequence	string	Image alt-text, grouped by section in order
section_image_captions	Sequence	string	Comma separated concatenated text from alt-text, attribution, and reference descriptions; this is how captions are formatted as input text when used

Loading the Data

Here we provide a small code snippet for how to load the TFRecord files. First, load any necessary packages.

import numpy as np
import glob
import tensorflow.compat.v1 as tf
from collections import defaultdict

Next, define a data parser class.

class DataParser():
  def __init__(self,
               filepath: str = 'wikiweb2m-*',
               path: str):
    self.filepath = filepath
    self.path = path
    self.data = defaultdict(list)

  def parse_data(self):
    context_feature_description = {
        'split': tf.io.FixedLenFeature([], dtype=tf.string),
        'page_title': tf.io.FixedLenFeature([], dtype=tf.string),
        'page_url': tf.io.FixedLenFeature([], dtype=tf.string),
        'clean_page_description': tf.io.FixedLenFeature([], dtype=tf.string),
        'raw_page_description': tf.io.FixedLenFeature([], dtype=tf.string),
        'is_page_description_sample': tf.io.FixedLenFeature([], dtype=tf.int64),
        'page_contains_images': tf.io.FixedLenFeature([], dtype=tf.int64),
        'page_content_sections_without_table_list': tf.io.FixedLenFeature([] , dtype=tf.int64)
    }

    sequence_feature_description = {
        'is_section_summarization_sample': tf.io.VarLenFeature(dtype=tf.int64),
        'section_title': tf.io.VarLenFeature(dtype=tf.string),
        'section_index': tf.io.VarLenFeature(dtype=tf.int64),
        'section_depth': tf.io.VarLenFeature(dtype=tf.int64),
        'section_heading_level': tf.io.VarLenFeature(dtype=tf.int64),
        'section_subsection_index': tf.io.VarLenFeature(dtype=tf.int64),
        'section_parent_index': tf.io.VarLenFeature(dtype=tf.int64),
        'section_text': tf.io.VarLenFeature(dtype=tf.string),
        'section_clean_1st_sentence': tf.io.VarLenFeature(dtype=tf.string),
        'section_raw_1st_sentence': tf.io.VarLenFeature(dtype=tf.string),
        'section_rest_sentence': tf.io.VarLenFeature(dtype=tf.string),
        'is_image_caption_sample': tf.io.VarLenFeature(dtype=tf.int64),
        'section_image_url': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_mime_type': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_width': tf.io.VarLenFeature(dtype=tf.int64),
        'section_image_height': tf.io.VarLenFeature(dtype=tf.int64),
        'section_image_in_wit': tf.io.VarLenFeature(dtype=tf.int64),
        'section_contains_table_or_list': tf.io.VarLenFeature(dtype=tf.int64),
        'section_image_captions': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_alt_text': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_raw_attr_desc': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_clean_attr_desc': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_raw_ref_desc': tf.io.VarLenFeature(dtype=tf.string),
        'section_image_clean_ref_desc': tf.io.VarLenFeature(dtype=tf.string),
        'section_contains_images': tf.io.VarLenFeature(dtype=tf.int64)
    }

    def _parse_function(example_proto):
      return tf.io.parse_single_sequence_example(example_proto,
                                                 context_feature_description,
                                                 sequence_feature_description)

    suffix = '.tfrecord*'

    data_path = glob.Glob(self.path + self.filepath + suffix)
    raw_dataset = tf.data.TFRecordDataset(data_path, compression_type='GZIP')
    parsed_dataset = raw_dataset.map(_parse_function)

    for d in parsed_dataset:
      split = d[0]['split'].numpy().decode()
      self.data[split].append(d)

Then you can run the following to parse the dataset.

parser = DataParser()
parser.parse_data()
print((len(parser.data['train']), len(parser.data['val']), len(parser.data['test'])))

Models

Our full attention, transient global, and prefix global experiments were run using the LongT5 code base.

How to Cite

If you extend or use this work, please cite the paper where it was introduced:

@inproceedings{
burns2023wiki,
title={A Suite of Generative Tasks for Multi-Level Multimodal Webpage Understanding},
author={Andrea Burns and Krishna Srinivasan and Joshua Ainslie and Geoff Brown and Bryan A. Plummer and Kate Saenko and Jianmo Ni and Mandy Guo},
booktitle={The 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)},
year={2023},
url={https://openreview.net/forum?id=rwcLHjtUmn}
}