SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization

Bogdan Gliwa, Iwona Mochol, Maciej Biesek, Aleksander Wawer


Abstract
This paper introduces the SAMSum Corpus, a new dataset with abstractive dialogue summaries. We investigate the challenges it poses for automated summarization by testing several models and comparing their results with those obtained on a corpus of news articles. We show that model-generated summaries of dialogues achieve higher ROUGE scores than the model-generated summaries of news – in contrast with human evaluators’ judgement. This suggests that a challenging task of abstractive dialogue summarization requires dedicated models and non-standard quality measures. To our knowledge, our study is the first attempt to introduce a high-quality chat-dialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.
Anthology ID:
D19-5409
Volume:
Proceedings of the 2nd Workshop on New Frontiers in Summarization
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | WS
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
70–79
Language:
URL:
https://aclanthology.org/D19-5409
DOI:
10.18653/v1/D19-5409
Bibkey:
Cite (ACL):
Bogdan Gliwa, Iwona Mochol, Maciej Biesek, and Aleksander Wawer. 2019. SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization. In Proceedings of the 2nd Workshop on New Frontiers in Summarization, pages 70–79, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization (Gliwa et al., EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-5409.pdf
Data
SAMSum Corpus