SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
This provides a new dataset for the research community to study abstractive summarization of dialogues, addressing a specific gap in NLP resources.
The authors introduced the SAMSum Corpus, a human-annotated dataset for abstractive dialogue summarization, and found that models achieve higher ROUGE scores on dialogues than news, but this contradicts human judgment, indicating the need for specialized models and metrics.
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.