Hacker1337/ru_dialogsum

---
language:
- ru
license: cc-by-nc-sa-4.0
size_categories:
- 10K<n<100K
task_categories:
- summarization
- text2text-generation
- text-generation
dataset_info:
features:
- name: dialogue
dtype: string
- name: summary
dtype: string
splits:
- name: train
num_bytes: 19849333
num_examples: 12460
- name: validation
num_bytes: 776937
num_examples: 500
- name: test
num_bytes: 2372057
num_examples: 1500
download_size: 10149385
dataset_size: 22998327
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: validation
path: data/validation-*
- split: test
path: data/test-*
---
This is machine translation from english to russian of a summarization dataset https://huggingface.co/datasets/knkarthick/dialogsum.

Translation was done by google translate, so the quality is sometimes arguable.

Project repository:
https://github.com/Hacker1337/tg_messages_summarizer

### Data Fields
- dialogue: text of dialogue.
- summary: summary of the dialogue.

---
license: cc-by-nc-sa-4.0
task_categories:
- summarization
language:
- ru
---

Information about data from the original dataset:
---

## 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
- **Point of Contact:** https://huggingface.co/knkarthick

### 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.

## Dataset Structure
### Data Instances
DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues (+1000 tests) split into train, test and validation.
The first instance in the training set:
{'id': 'train_0', 'summary': "Mr. Smith's getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkins'll give some information about their classes and medications to help Mr. Smith quit smoking.", 'dialogue': "#Person1#: Hi, Mr. Smith. I'm Doctor Hawkins. Why are you here today?\n#Person2#: I found it would be a good idea to get a check-up.\n#Person1#: Yes, well, you haven't had one for 5 years. You should have one every year.\n#Person2#: I know. I figure as long as there is nothing wrong, why go see the doctor?\n#Person1#: Well, the best way to avoid serious illnesses is to find out about them early. So try to come at least once a year for your own good.\n#Person2#: Ok.\n#Person1#: Let me see here. Your eyes and ears look fine. Take a deep breath, please. Do you smoke, Mr. Smith?\n#Person2#: Yes.\n#Person1#: Smoking is the leading cause of lung cancer and heart disease, you know. You really should quit.\n#Person2#: I've tried hundreds of times, but I just can't seem to kick the habit.\n#Person1#: Well, we have classes and some medications that might help. I'll give you more information before you leave.\n#Person2#: Ok, thanks doctor.", 'topic': "get a check-up}
### 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
## 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.

原始信息汇总

数据集概述

基本信息

• 语言: 俄语
• 许可: CC BY-NC-SA 4.0
• 数据规模: 10K<n<100K
• 任务类别:
  • 摘要生成
  • 文本到文本生成
  • 文本生成

数据结构

• 特征:
  • dialogue: 字符串类型，对话文本
  • summary: 字符串类型，对话摘要

数据分割

• 训练集:
  • 字节数: 19849333
  • 样本数: 12460
• 验证集:
  • 字节数: 776937
  • 样本数: 500
• 测试集:
  • 字节数: 2372057
  • 样本数: 1500

数据文件

• 配置: default
  • 训练集: data/train-*
  • 验证集: data/validation-*
  • 测试集: data/test-*

数据字段

• dialogue: 对话文本
• summary: 对话摘要