Update README.md

README.md

@@ -1,3 +1,199 @@
 ---
 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](https://arxiv.org/abs/2305.03668)
+`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](https://github.com/google-research-datasets/wit/blob/main/wikiweb2m.md).
+
+## 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.
+
+![Illustration of Succulents Wikipedia Article being used for page description generation, section summarization, and contextual image captioning](images/wikiweb2m_image.png)
+
+
+
+## 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.
+
+```python
+import numpy as np
+import glob
+import tensorflow.compat.v1 as tf
+from collections import defaultdict
+```
+
+Next, define a data parser class.
+```python
+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.
+```python
+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](https://github.com/google-research/longt5) code base.
+
+
+## How to Cite
+
+If you extend or use this work, please cite the [paper](https://arxiv.org/abs/2305.03668) 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}
+}
+```