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.

本研究介绍了 SAMSum 语料库，这是一个新的抽象对话摘要数据集，研究人员使用多个模型进行测试，并将其结果与新闻文章语料库的结果进行比较，结果显示与人类评估者的判断相反，模型生成的对话摘要具有更高的 ROUGE 分数，这表明需要使用专用模型和非标准质量度量的抽象对话摘要这一具有挑战性的任务，据我们所知，这是引入高质量聊天对话语料库的第一次尝试，并手动注释了抽象摘要，可供研究人员进行进一步研究。