Automated documentation update.

PiperOrigin-RevId: 798159149

Author: The TensorFlow Datasets Authors

docs/catalog/_toc.yaml

@@ -22,6 +22,8 @@ toc:
     title: billsum
   - path: /datasets/catalog/booksum
     title: booksum (manual)
+  - path: /datasets/catalog/multi_news
+    title: multi_news
   - path: /datasets/catalog/newsroom
     title: newsroom (manual)
   - path: /datasets/catalog/reddit
@@ -815,6 +817,8 @@ toc:
     title: math_qa
   - path: /datasets/catalog/mlqa
     title: mlqa
+  - path: /datasets/catalog/multi_news
+    title: multi_news
   - path: /datasets/catalog/natural_instructions
     title: natural_instructions
   - path: /datasets/catalog/natural_questions
@@ -862,6 +866,10 @@ toc:
   - path: /datasets/catalog/glove100_angular
     title: glove100_angular
   title: Nearest neighbors
+- section:
+  - path: /datasets/catalog/multi_news
+    title: multi_news
+  title: News
 - section:
   - path: /datasets/catalog/coco
     title: coco
@@ -1304,8 +1312,6 @@ toc:
     title: gigaword
   - path: /datasets/catalog/gov_report
     title: gov_report
-  - path: /datasets/catalog/multi_news
-    title: multi_news
   - path: /datasets/catalog/wikihow
     title: wikihow (manual)
   - path: /datasets/catalog/xsum
@@ -1475,6 +1481,8 @@ toc:
     title: movie_rationales
   - path: /datasets/catalog/mrqa
     title: mrqa
+  - path: /datasets/catalog/multi_news
+    title: multi_news
   - path: /datasets/catalog/multi_nli
     title: multi_nli
   - path: /datasets/catalog/multi_nli_mismatch
@@ -1747,6 +1755,8 @@ toc:
     title: booksum (manual)
   - path: /datasets/catalog/databricks_dolly
     title: databricks_dolly
+  - path: /datasets/catalog/multi_news
+    title: multi_news
   - path: /datasets/catalog/newsroom
     title: newsroom (manual)
   - path: /datasets/catalog/reddit

docs/catalog/multi_news.md

@@ -3,7 +3,7 @@
     <meta itemprop="name" content="TensorFlow Datasets" />
   </div>
   <meta itemprop="name" content="multi_news" />
-  <meta itemprop="description" content="Multi-News, consists of news articles and human-written summaries&#10;of these articles from the site newser.com.&#10;Each summary is professionally written by editors and&#10;includes links to the original articles cited.&#10;&#10;There are two features:&#10;  - document: text of news articles seperated by special token &quot;|||||&quot;.&#10;  - summary: news summary.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;multi_news&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10;  print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;" />
+  <meta itemprop="description" content="# Multi-News Dataset&#10;&#10;Multi-News consists of news articles and human-written summaries of these&#10;articles from the news site `newser.com`. Each summary is professionally written&#10;by editors and includes links to the original articles cited.&#10;&#10;This is the first large-scale dataset for multi-document summarization on news&#10;articles.&#10;&#10;Each record has two features:&#10;&#10;* `document`: Texts of news articles, separated by special token &quot;|||||&quot;.&#10;* `summary`: Summary of the news.&#10;&#10;To use this dataset:&#10;&#10;```python&#10;import tensorflow_datasets as tfds&#10;&#10;ds = tfds.load(&#x27;multi_news&#x27;, split=&#x27;train&#x27;)&#10;for ex in ds.take(4):&#10;  print(ex)&#10;```&#10;&#10;See [the guide](https://www.tensorflow.org/datasets/overview) for more&#10;informations on [tensorflow_datasets](https://www.tensorflow.org/datasets).&#10;&#10;" />
   <meta itemprop="url" content="https://www.tensorflow.org/datasets/catalog/multi_news" />
   <meta itemprop="sameAs" content="https://github.com/Alex-Fabbri/Multi-News" />
   <meta itemprop="citation" content="@misc{alex2019multinews,&#10;    title={Multi-News: a Large-Scale Multi-Document Summarization Dataset and Abstractive Hierarchical Model},&#10;    author={Alexander R. Fabbri and Irene Li and Tianwei She and Suyi Li and Dragomir R. Radev},&#10;    year={2019},&#10;    eprint={1906.01749},&#10;    archivePrefix={arXiv},&#10;    primaryClass={cs.CL}&#10;}" />
@@ -12,14 +12,26 @@
 # `multi_news`
 
 
+Note: This dataset has been updated since the last stable release. The new
+versions and config marked with
+<span class="material-icons" title="Available only in the tfds-nightly package">nights_stay</span>
+are only available in the `tfds-nightly` package.
+
 *   **Description**:
 
-Multi-News, consists of news articles and human-written summaries of these
-articles from the site newser.com. Each summary is professionally written by
-editors and includes links to the original articles cited.
+# Multi-News Dataset
+
+Multi-News consists of news articles and human-written summaries of these
+articles from the news site `newser.com`. Each summary is professionally written
+by editors and includes links to the original articles cited.
+
+This is the first large-scale dataset for multi-document summarization on news
+articles.
+
+Each record has two features:
 
-There are two features: - document: text of news articles seperated by special
-token "|||||". - summary: news summary.
+*   `document`: Texts of news articles, separated by special token "|||||".
+*   `summary`: Summary of the news.
 
 *   **Additional Documentation**:
     <a class="button button-with-icon" href="https://paperswithcode.com/dataset/multi-news">
@@ -31,15 +43,21 @@ token "|||||". - summary: news summary.
     [https://github.com/Alex-Fabbri/Multi-News](https://github.com/Alex-Fabbri/Multi-News)
 
 *   **Source code**:
-    [`tfds.summarization.MultiNews`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/summarization/multi_news.py)
+    [`tfds.datasets.multi_news.Builder`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/datasets/multi_news/multi_news_dataset_builder.py)
 
 *   **Versions**:
 
-    *   **`1.0.0`** (default): No release notes.
+    *   `1.0.0`: Initial release.
+    *   `2.0.0`: [Do not use] Update the dataset with valid URLs.
+    *   **`2.1.0`** (default)
+        <span class="material-icons" title="Available only in the tfds-nightly package">nights_stay</span>:
+        Update the dataset with the correct URLs. The URLs in this version come
+        from HuggingFace's dataset repo, which is curated by the same author:
+        https://huggingface.co/datasets/alexfabbri/multi_news.
 
-*   **Download size**: `245.06 MiB`
+*   **Download size**: `721.73 MiB`
 
-*   **Dataset size**: `669.80 MiB`
+*   **Dataset size**: `666.50 MiB`
 
 *   **Auto-cached**
     ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):
@@ -88,7 +106,7 @@ summary  | Text         |       | string |
 <button id="displaydataframe">Display examples...</button>
 <div id="dataframecontent" style="overflow-x:auto"></div>
 <script>
-const url = "https://storage.googleapis.com/tfds-data/visualization/dataframe/multi_news-1.0.0.html";
+const url = "https://storage.googleapis.com/tfds-data/visualization/dataframe/multi_news-2.1.0.html";
 const dataButton = document.getElementById('displaydataframe');
 dataButton.addEventListener('click', async () => {
   // Disable the button after clicking (dataframe loaded only once).

docs/catalog/overview.md

@@ -40,6 +40,7 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`aeslc`](aeslc.md)
 *   [`billsum`](billsum.md)
 *   [`booksum`](booksum.md)
+*   [`multi_news`](multi_news.md)
 *   [`newsroom`](newsroom.md)
 *   [`reddit`](reddit.md)
 *   [`reddit_tifu`](reddit_tifu.md)
@@ -515,6 +516,7 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`math_dataset`](math_dataset.md)
 *   [`math_qa`](math_qa.md)
 *   [`mlqa`](mlqa.md)
+*   [`multi_news`](multi_news.md)
 *   [`natural_instructions`](natural_instructions.md)
 *   [`natural_questions`](natural_questions.md)
 *   [`natural_questions_open`](natural_questions_open.md)
@@ -541,6 +543,10 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`deep1b`](deep1b.md)
 *   [`glove100_angular`](glove100_angular.md)
 
+### `News`
+
+*   [`multi_news`](multi_news.md)
+
 ### `Object detection`
 
 *   [`coco`](coco.md)
@@ -808,7 +814,6 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`covid19sum`](covid19sum.md)
 *   [`gigaword`](gigaword.md)
 *   [`gov_report`](gov_report.md)
-*   [`multi_news`](multi_news.md)
 *   [`wikihow`](wikihow.md)
 *   [`xsum`](xsum.md)
 
@@ -900,6 +905,7 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`mlqa`](mlqa.md)
 *   [`movie_rationales`](movie_rationales.md)
 *   [`mrqa`](mrqa.md)
+*   [`multi_news`](multi_news.md)
 *   [`multi_nli`](multi_nli.md)
 *   [`multi_nli_mismatch`](multi_nli_mismatch.md)
 *   [`natural_instructions`](natural_instructions.md)
@@ -1046,6 +1052,7 @@ for ex in tfds.load('cifar10', split='train'):
 *   [`billsum`](billsum.md)
 *   [`booksum`](booksum.md)
 *   [`databricks_dolly`](databricks_dolly.md)
+*   [`multi_news`](multi_news.md)
 *   [`newsroom`](newsroom.md)
 *   [`reddit`](reddit.md)
 *   [`reddit_tifu`](reddit_tifu.md)