Datasets:

d0rj
/

dialogsum-ru

	---
	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
	---
	# 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.