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

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arxiv 1911.12237 v2 pith:7VJYEQYX submitted 2019-11-27 cs.CL

Bogdan Gliwa , Iwona Mochol , Maciej Biesek , Aleksander Wawer This is my paper · ORCID
classification cs.CL
keywords abstractivecorpusdialoguesummariessummarizationdatasetmodel-generatedmodels
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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