The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization
Open accessAbstract
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 chatdialogues corpus, manually annotated with abstractive summarizations, which can be used by the research community for further studies.
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“Previous research finds taking the longest sentence20 as dialogue summarization is a good baseline.”
From Aci-bench: a Novel Ambient Clinical Intelligence Dataset for Benchmarking Automatic Visit Note Generation (2023) · cites this paper as [20]
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Metadata
- Authors
- Gliwa, B., Mochol, I., Biesek, M., & Wawer, A.
- Affiliations
- Samsung (Poland) (PL)
- Venue
- Proceedings of the 2nd Workshop on New Frontiers in Summarization
- Published
- 2019
- Pages
- 70-79
- OpenAlex
- W2989743967
- Cited by
- 424
- References
- 24
- Citations by year
- 2026: 30 · 2025: 72 · 2024: 52 · 2023: 125 · 2022: 72 · 2021: 67 · 2020: 6
- FWCI
- 13.57
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by
- Locations
- Workshop on New Frontiers in Summarization (NewSum) (publishedVersion, cc-by)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Speech and dialogue systems
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Text Analysis Techniques
- Keywords
- abstractive summarization, dialogue summarization, news summarization
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