knkarthick/dialogsum

---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license: cc-by-nc-sa-4.0
multilinguality:
- monolingual
size_categories:
- 10K<n<100K
source_datasets:
- original
task_categories:
- summarization
- text2text-generation
- text-generation
task_ids: []
pretty_name: DIALOGSum Corpus
tags:
- dialogue-summary
- one-liner-summary
- meeting-title
- email-subject
---
# 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.

原始信息汇总

DIALOGSum Corpus 数据集概述

数据集描述

数据集概要

• 名称: DialogSum
• 类型: 对话摘要数据集
• 规模: 包含13,460个对话及其对应的人工标注摘要和主题，额外有100个用于主题生成的保留数据。

语言

• 主要语言: 英语

数据集结构

数据实例

• 总数: 13,460个对话，分为训练集、测试集和验证集。
• 训练集示例:
  • id: train_0
  • summary: "Mr. Smiths getting a check-up, and Doctor Hawkins advises him to have one every year. Hawkinsll give some information about their classes and medications to help Mr. Smith quit smoking."
  • dialogue: 对话文本
  • topic: "get a check-up"

数据字段

• dialogue: 对话文本
• summary: 人工编写的对话摘要
• topic: 人工编写的对话主题/一句话摘要
• id: 示例的唯一文件ID

数据分割

• 训练集: 12,460
• 验证集: 500
• 测试集: 1,500
• 保留集: 100（仅包含id, dialogue, topic三个特征）

数据集创建

采集理由

• 数据来源于三个公共对话语料库及一个英语口语练习网站，覆盖日常生活的广泛话题。
• 对话具有丰富的现实生活场景，清晰的沟通模式和意图，适合作为自动摘要的来源。

标注者

• 语言生成者: 语言学家
• 标注者: 语言专家

许可信息

• 许可证: CC BY-NC-SA 4.0