docs: update README

README.md

@@ -1,4 +1,27 @@
 ---
+annotations_creators:
+- expert-generated
+language_creators:
+- translated
+language:
+- ru
+license:
+- mit
+multilinguality:
+- monolingual
+size_categories:
+- 10K<n<100K
+source_datasets:
+- knkarthick/dialogsum
+task_categories:
+- summarization
+- text2text-generation
+- text-generation
+task_ids: []
+pretty_name: DIALOGSum Corpus (ru)
+tags:
+- conversations-summarization
+- dialogue-summarization
 dataset_info:
   features:
   - name: id
@@ -21,7 +44,81 @@ dataset_info:
     num_examples: 1500
   download_size: 10144708
   dataset_size: 22143849
+train-eval-index:
+  - config: samsum
+    task: summarization
+    task_id: summarization
+    splits:
+      eval_split: test
+    col_mapping:
+      dialogue: text
+      summary: target
 ---
-# Dataset Card for "dialogsum-ru"
+# Dataset Card for DIALOGSum Corpus
+## Dataset Description
+### Links
+- **Homepage:** https://aclanthology.org/2021.findings-acl.449
+- **Repository:** https://github.com/cylnlp/dialogsum
+- **Paper:** https://aclanthology.org/2021.findings-acl.449
 
-[More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)
+### Dataset Summary
+DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 (Plus 100 holdout data for topic generation) dialogues with corresponding manually labeled summaries and topics.
+### Languages
+Russian (translated from English by Google Translate).
+
+## Dataset Structure
+### Data Fields
+- dialogue: text of dialogue.
+- summary: human written summary of the dialogue.
+- topic: human written topic/one liner of the dialogue.
+- id: unique file id of an example.
+
+### Data Splits
+- train: 12460
+- val: 500
+- test: 1500
+- holdout: 100 [Only 3 features: id, dialogue, topic]
+
+## Dataset Creation
+### Curation Rationale
+In paper:
+We collect dialogue data for DialogSum from three public dialogue corpora, namely Dailydialog (Li et al., 2017), DREAM (Sun et al., 2019) and MuTual (Cui et al., 2019), as well as an English speaking practice website. These datasets contain face-to-face spoken dialogues that cover a wide range of daily-life topics, including schooling, work, medication, shopping, leisure, travel. Most conversations take place between friends, colleagues, and between service providers and customers.
+
+Compared with previous datasets, dialogues from DialogSum have distinct characteristics:
+
+Under rich real-life scenarios, including more diverse task-oriented scenarios;
+Have clear communication patterns and intents, which is valuable to serve as summarization sources;
+Have a reasonable length, which comforts the purpose of automatic summarization.
+
+We ask annotators to summarize each dialogue based on the following criteria:
+Convey the most salient information;
+Be brief;
+Preserve important named entities within the conversation;
+Be written from an observer perspective;
+Be written in formal language.
+### Who are the source language producers?
+linguists
+### Who are the annotators?
+language experts
+
+## Licensing Information
+MIT License
+## Citation Information
+```
+@inproceedings{chen-etal-2021-dialogsum,
+    title = "{D}ialog{S}um: {A} Real-Life Scenario Dialogue Summarization Dataset",
+    author = "Chen, Yulong  and
+      Liu, Yang  and
+      Chen, Liang  and
+      Zhang, Yue",
+    booktitle = "Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021",
+    month = aug,
+    year = "2021",
+    address = "Online",
+    publisher = "Association for Computational Linguistics",
+    url = "https://aclanthology.org/2021.findings-acl.449",
+    doi = "10.18653/v1/2021.findings-acl.449",
+    pages = "5062--5074",
+```
+## Contributions
+Thanks to [@cylnlp](https://github.com/cylnlp) for adding this dataset.