# 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

### 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.
task_categories:summarization task_categories:text2text-generation task_categories:text-generation annotations_creators:expert-generated language_creators:translated multilinguality:monolingual size_categories:10K<n<100K source_datasets:knkarthick/dialogsum language:ru license:mit conversations-summarization dialogue-summarization region:us
{"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", "dtype": "string"}, {"name": "dialogue", "dtype": "string"}, {"name": "summary", "dtype": "string"}, {"name": "topic", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 19115158, "num_examples": 12460}, {"name": "validation", "num_bytes": 746312, "num_examples": 500}, {"name": "test", "num_bytes": 2282379, "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"}}]}
TAGS
#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #annotations_creators-expert-generated #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-knkarthick/dialogsum #language-Russian #license-mit #conversations-summarization #dialogue-summarization #region-us 

# Dataset Card for DIALOGSum Corpus
## Dataset Description
### Links
- Homepage: URL
- Repository: URL
- Paper: URL

### 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


## Contributions
Thanks to @cylnlp for adding this dataset.
# Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links
- Homepage: URL
- Repository: URL
- Paper: URL ### 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 ## Contributions
Thanks to @cylnlp for adding this dataset.
TAGS
#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #annotations_creators-expert-generated #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-knkarthick/dialogsum #language-Russian #license-mit #conversations-summarization #dialogue-summarization #region-us 
 # Dataset Card for DIALOGSum Corpus ## Dataset Description ### Links
- Homepage: URL
- Repository: URL
- Paper: URL ### 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 ## Contributions
Thanks to @cylnlp for adding this dataset.
passage: TAGS
#task_categories-summarization #task_categories-text2text-generation #task_categories-text-generation #annotations_creators-expert-generated #language_creators-translated #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-knkarthick/dialogsum #language-Russian #license-mit #conversations-summarization #dialogue-summarization #region-us 
# Dataset Card for DIALOGSum Corpus## Dataset Description### Links
- Homepage: URL
- Repository: URL
- Paper: URL### 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