DialogSum: A Real-life Scenario Dialogue Summarization Dataset

DialogSum is a large-scale dialogue summarization dataset, consisting of 13,460 dialogues with corresponding manually labeled summaries and topics. You can directly download the data from this fold, or from the Hugging face Dataset.

This work is accepted by ACL findings 2021. You may find the paper here. If you use our dataset, please kindly cite our paper:

@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",
}

DialogSum Challenge is a shared task at INLG 2022. Check the task website and shared task report.

This dataset is under CC BY-NC-SA 4.0 license. You may not use it for commercial use without permission.

Quick Start

We provide a BART baseline for dialogue summarization, which may help you explore this area as a quick start. See Baseline.

Dialogue Data

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.

Summaries

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.

Topics

In addition to summaries, we also ask annotators to write a short topic for each dialogue, which can be potentially useful for future work, e.g. generating summaries by leveraging topic information.

Reference

• Leyang Cui, Yu Wu, Shujie Liu, Yue Zhang, and Ming Zhou. 2020. MuTual: A dataset for multi-turn dialogue reasoning. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 1406–1416, Online. Association for Computational Linguistics.

• Yanran Li, Hui Su, Xiaoyu Shen, Wenjie Li, Ziqiang Cao, and Shuzi Niu. 2017. DailyDialog: A manually labelled multi-turn dialogue dataset. In Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 986–995, Taipei, Taiwan. Asian Federation of Natural Language Processing.

• Kai Sun, Dian Yu, Jianshu Chen, Dong Yu, Yejin Choi,and Claire Cardie. 2019. DREAM: A challenge dataset and models for dialogue-based reading comprehension. Transactions of the Association for Computational Linguistics, 7:217–231.