metadata

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

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 for adding this dataset.

Datasets:

d0rj
/

dialogsum-ru