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

**Authors:** Bogdan Gliwa, Iwona Mochol, Maciej Biesek, Aleksander Wawer  
**arXiv:** [1911.12237](https://arxiv.org/abs/1911.12237)  
**Date:** 2019-11-27  
**Summary page:** https://www.summarizepaper.com/fr/paper/1911.12237v2/

## 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.