metadata

annotations_creators:
  - expert-generated
language_creators:
  - expert-generated
languages:
  - en
licenses:
  - cc-by-nc-nd-4.0
multilinguality:
  - monolingual
size_categories:
  - 100K<n<1M
source_datasets:
  - original
task_categories:
  - summarization
  - topic modeling
  - one liner summary
  - email subject
  - meeting title
task_ids:
  - summarization-other-conversations-summarization
paperswithcode_id: samsum-corpus
pretty_name: XSum Corpus

Dataset Card for SAMSum Corpus

Dataset Description

Dataset Summary

The SAMSum dataset contains about 16k messenger-like conversations with summaries. Conversations were created and written down by linguists fluent in English. Linguists were asked to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. The style and register are diversified - conversations could be informal, semi-formal or formal, they may contain slang words, emoticons and typos. Then, the conversations were annotated with summaries. It was assumed that summaries should be a concise brief of what people talked about in the conversation in third person. The SAMSum dataset was prepared by Samsung R&D Institute Poland and is distributed for research purposes (non-commercial licence: CC BY-NC-ND 4.0).

Languages

English

Dataset Structure

Data Instances

SAMSum dataset is made of 16369 conversations distributed uniformly into 4 groups based on the number of utterances in con- versations: 3-6, 7-12, 13-18 and 19-30. Each utterance contains the name of the speaker. Most conversations consist of dialogues between two interlocutors (about 75% of all conversations), the rest is between three or more people The first instance in the training set: {'id': '13818513', 'summary': 'Amanda baked cookies and will bring Jerry some tomorrow.', 'dialogue': "Amanda: I baked cookies. Do you want some?\r\nJerry: Sure!\r\nAmanda: I'll bring you tomorrow :-)"}

Data Fields

dialogue: text of dialogue.
summary: human written summary of the dialogue.
id: unique file id of an example.

Data Splits

train: 14732
val: 818
test: 819

Dataset Creation

Curation Rationale

In paper: In the first approach, we reviewed datasets from the following categories: chatbot dialogues, SMS corpora, IRC/chat data, movie dialogues, tweets, comments data (conversations formed by replies to comments), transcription of meetings, written discussions, phone dialogues and daily communication data. Unfortunately, they all differed in some respect from the conversations that are typically written in messenger apps, e.g. they were too technical (IRC data), too long (comments data, transcription of meetings), lacked context (movie dialogues) or they were more of a spoken type, such as a dialogue between a petrol station assistant and a client buying petrol. As a consequence, we decided to create a chat dialogue dataset by constructing such conversations that would epitomize the style of a messenger app.

Who are the source language producers?

linguists

Who are the annotators?

language experts

Annotation process

In paper: Each dialogue was created by one person. After collecting all of the conversations, we asked language experts to annotate them with summaries, assuming that they should (1) be rather short, (2) extract important pieces of information, (3) include names of interlocutors, (4) be written in the third person. Each dialogue contains only one reference summary.

Licensing Information

non-commercial licence: MIT

Citation Information

@InProceedings{xsum-emnlp,
  author =      "Shashi Narayan and Shay B. Cohen and Mirella Lapata",
  title =       "Don't Give Me the Details, Just the Summary! {T}opic-Aware Convolutional Neural Networks for Extreme Summarization",
  booktitle =   "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing ",
  year =        "2018",
  address =     "Brussels, Belgium",

Contributions

Thanks to @Edinburgh NLP for adding this dataset.

Datasets:

knkarthick
/

xsum