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arXiv · 1911.12237

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

Bogdan Gliwa·Iwona Mochol·Maciej Biesek·Aleksander Wawer
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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.

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Bogdan Gliwa, Iwona Mochol, Maciej Biesek, Aleksander Wawer. 2019-11-29. SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization. https://doi.org/10.18653/v1%2Fd19-5409

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