dialogsum

# 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
- **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.
### Languages
English

## 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
- 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
CC BY-NC-SA 4.0
## 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.

# DIALOGSum Corpus 数据集卡片
## 数据集描述
### 相关链接
- **主页:** https://aclanthology.org/2021.findings-acl.449
- **代码仓库:** https://github.com/cylnlp/dialogsum
- **论文:** https://aclanthology.org/2021.findings-acl.449
- **联系方式:** https://huggingface.co/knkarthick

### 数据集概览
DialogSum是一款大规模对话摘要（dialogue summarization）数据集，包含13460段对话（外加100条用于主题生成的留存数据），均配有人工标注的摘要与主题。
### 语言
英语

## 数据集结构
### 数据实例
DialogSum是一款大规模对话摘要数据集，包含13460段对话（+1000条测试数据），划分为训练集、测试集与验证集。
训练集的首个样本如下：

{'id': 'train_0', 'summary': "史密斯先生正在进行体检，霍金斯医生建议他每年进行一次体检。霍金斯医生将介绍相关课程与药物方案，帮助史密斯先生戒烟。", 'dialogue": "#Person1#: 您好，史密斯先生，我是霍金斯医生。您今天来是有什么事吗？
#Person2#: 我觉得做个体检是个不错的选择。
#Person1#: 没错，您已经有5年没做过体检了，建议您每年都做一次。
#Person2#: 我知道，我想着只要没什么不舒服，何必去看医生呢。
#Person1#: 其实，规避重症的最佳方式就是早发现早治疗。所以为了您的健康，尽量每年至少来检查一次。
#Person2#: 好的。
#Person1#: 让我看看，您的眼耳状况看起来不错。请深呼吸一下。您抽烟吗，史密斯先生？
#Person2#: 抽的。
#Person1#: 您要知道，吸烟是肺癌与心脏病的主要诱因，您真的应该戒烟。
#Person2#: 我试过好多次了，但就是戒不掉这个习惯。
#Person1#: 我们有相关课程和药物或许能帮到您，您离开前我会给您详细介绍一下。
#Person2#: 好的，谢谢医生。", 'topic': "进行体检"}

### 数据字段
- dialogue: 对话文本内容
- summary: 人工撰写的对话摘要
- topic: 人工撰写的对话主题/一句话概括
- id: 样本的唯一文件标识符

### 数据划分
- 训练集（train）: 12460
- 验证集（val）: 500
- 测试集（test）: 1500
- 留存集（holdout）: 100 [仅包含id、dialogue、topic三个特征]

## 数据集构建
### 构建依据
在论文中，本数据集的对话数据来源于三个公开对话语料库，分别是Dailydialog（Li等人，2017）、DREAM（Sun等人，2019）与MuTual（Cui等人，2019），以及一个英语口语练习网站。这些语料包含覆盖多元日常话题的面对面口语对话，涵盖学业、工作、医疗、购物、休闲、旅行等场景，多数对话发生在朋友、同事之间，以及服务提供者与客户之间。

相较于此前的对话数据集，DialogSum的对话具备以下鲜明特征：
1. 涵盖丰富的真实生活场景，包含更多样化的任务导向型（task-oriented）场景；
2. 具备清晰的沟通模式与对话意图，可作为优质的摘要生成源；
3. 长度适中，适配自动摘要（automatic summarization）任务的需求。

我们要求标注人员基于以下标准撰写每段对话的摘要：
- 传达最核心的关键信息；
- 表述简洁凝练；
- 保留对话中的重要命名实体（named entities）；
- 以旁观者视角进行撰写；
- 使用正式书面语言。

### 源语言生产者
语言学家
### 标注人员
语言专家

## 授权信息
CC BY-NC-SA 4.0（知识共享署名-非商业性使用-相同方式共享4.0协议）
## 引用信息

@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",

## 贡献致谢
感谢 [@cylnlp](https://github.com/cylnlp) 贡献本数据集。