--- dataset_info: features: - name: index dtype: int64 - name: id dtype: string - name: dialogue dtype: string - name: summary dtype: string - name: topic dtype: string splits: - name: train num_bytes: 27913254 num_examples: 24813 download_size: 13968520 dataset_size: 27913254 configs: - config_name: default data_files: - split: train path: data/train-* license: cc-by-nc-2.0 task_categories: - summarization - conversational language: - ar pretty_name: ar messum size_categories: - 10K 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 typ- ically 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 assis- tant and a client buying petrol. As a consequence, we decided to create a chat dialogue dataset by constructing such conversa- tions that would epitomize the style of a messenger app. ### Source Data #### Initial Data Collection and Normalization In paper: > We asked linguists to create conversations similar to those they write on a daily basis, reflecting the proportion of topics of their real-life messenger conversations. It includes chit-chats, gossiping about friends, arranging meetings, discussing politics, consulting university assignments with colleagues, etc. Therefore, this dataset does not contain any sensitive data or fragments of other corpora. #### Who are the source language producers? linguists ### Annotations #### 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 ref- erence summary. #### Who are the annotators? language experts ### Personal and Sensitive Information None, see above: Initial Data Collection and Normalization ## Considerations for Using the Data ### Social Impact of Dataset [Needs More Information] ### Discussion of Biases [Needs More Information] ### Other Known Limitations [Needs More Information] ## Additional Information ### Dataset Curators [Needs More Information] ### Licensing Information non-commercial licence: CC BY-NC-ND 4.0 ### Citation Information ``` @inproceedings{gliwa-etal-2019-samsum, title = "{SAMS}um Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization", author = "Gliwa, Bogdan and Mochol, Iwona and Biesek, Maciej and Wawer, Aleksander", booktitle = "Proceedings of the 2nd Workshop on New Frontiers in Summarization", month = nov, year = "2019", address = "Hong Kong, China", publisher = "Association for Computational Linguistics", url = "https://www.aclweb.org/anthology/D19-5409", doi = "10.18653/v1/D19-5409", pages = "70--79" } ``` ### Contributions Thanks to [@cccntu](https://github.com/cccntu) for adding this dataset. [More Information needed](https://github.com/huggingface/datasets/blob/main/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)